Yazar "Turkoglu, Muammer" seçeneğine göre listele

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    Apricot Disease Identification based on Attributes Obtained from Deep Learning Algorithms
    (Ieee, 2018) Turkoglu, Muammer; Hanbay, Davut
    In recent years, deep learning widely used in image processing field, has introduced many new applications related to the agricultural field. In this study, for apricot disease detection were used deep learning models such as AlexNet, Vgg16, and Vgg19 based on pre-trained deep Convolutional Neural Networks (CNN). The deep attributes obtained from these models are classified by K-Nearest Neighbour (KNN) method. To calculate the performance of the proposed methods was applied 10- fold cross-validation test. The dataset consists of 960 images including healthy and diseased apricot images. According to the obtained results, the highest accuracy was obtained as 94.8% by using Vgg16 model.
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    Classification of The Grape Varieties based on Leaf Recognition by Using SVM Classifier
    (Ieee, 2015) Turkoglu, Muammer; Hanbay, Davut
    In this paper, to classify the grape tree species, the extracted features from leaf images are classified using a multi-class support vector machines. Feature extraction stage, the grape leafs are calculated by using 9 different features. Image processing stage involves gray tone dial, median filtering, contrast, thresh holding and morphological-logical processes. In the classification stage, the obtained properties with the help of multi-class support vector machines (MCSVM) is performed classification process. In the testing phase, by applying the different leaf images is calculated the performance of model. In this study, MATLAB software was used. At the end of the test was determined the total success rate of 90.7%.
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    Combination of Deep Features and KNN Algorithm for Classification of Leaf-Based Plant Species
    (Ieee, 2019) Turkoglu, Muammer; Hanbay, Davut
    Recently, Convolutional Neural Networks (CNN), which is used in the solution of many image processing problems, has been used successfully for many problems in the agricultural field. In this study, for classification of plant species is proposed an approach based on the combination of deep architectures. Deep features were extracted from the plant leaves using the fc6 layer of the previously trained AlexNet and VGG16 models. Then, the reduction of the number of deep features by using the Principal Component Analysis (PCA) method was done quickly and the best distinguishing features were obtained. Finally, the classification performances were calculated using the K-Nearest Neighbor (KNN) method. Flavia and Swedish plant leaf data sets were used to test the proposed system. According to the experimental results, the accuracy scores for Flavia and Swedish data sets was obtained as 99.42% and 99.64%, respectively.
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    A deep feature extractor approach for the recognition of pollen-bearing bees
    (Ieee, 2020) Turkoglu, Muammer; Uzen, Huseyin; Hanbay, Davut
    In this study, a convolutional neural network (ESA) based feature extracting hybrid model was proposed for the identification of bees carrying pollen or not. The fc6 and fc7 layers of AlexNet and VGG16 which a pre-trained ESA architecture, were used as feature extractors. The performances of the different combinations of the deep properties obtained using the SVM classifier were calculated. The PollenDataset dataset was used to test the proposed model. According to the experimental results, an accuracy score of 97.20% was obtained. As a result, the obtained accuracy score was compared with the state-of-the-art accuracy scores and the proposed model provided better performance than the compared methods.
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    Depth-wise Squeeze and Excitation Block-based Efficient-Unet model for surface defect detection
    (Springer, 2023) Uzen, Huseyin; Turkoglu, Muammer; Aslan, Muzaffer; Hanbay, Davut
    Detection of surface defects in manufacturing systems is crucial for product quality. Detection of surface defects with high accuracy can prevent financial and time losses. Recently, efforts to develop high-performance automatic surface defect detection systems using computer vision and machine-learning methods have become prominent. In line with this purpose, this paper proposed a novel approach based on Depth-wise Squeeze and Excitation Block-based Efficient-Unet (DSEB-EUNet) for automatic surface defect detection. The proposed model consists of an encoder-decoder, the basic structure of the Unet architecture, and a Depth-wise Squeeze and Excitation Block added to the skip-connection of Unet. First, in the encoder part of the proposed model, low-level and high-level features were obtained by the EfficientNet network. Then, these features were transferred to the Depth-wise Squeeze and Excitation Block. The proposed DSEB based on the combination of Squeeze-Excitation and Depth-wise Separable Convolution enabled to reveal of critical information by weighting the features with a lightweight gating mechanism for surface defect detection. Besides, in the decoder part of the proposed model, the structure called Multi-level Feature Concatenated Block (MFCB) transferred the weighted features to the last layers without losing spatial detail. Finally, pixel-level defect detection was performed using the sigmoid function. The proposed model was tested using three general datasets for surface defect detection. In experimental works, the best F1-scores for MT, DAGM, and AITEX datasets using the proposed DSEB-EUNet architecture were 89.20%, 85.97%, and 90.39%, respectively. These results showed the proposed model outperforms higher performance compared to state-of-the-art approaches.
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    InceptionV3 based enriched feature integration network architecture for pixel-level surface defect detection
    (Gazi Univ, Fac Engineering Architecture, 2023) Uzen, Huseyin; Turkoglu, Muammer; Ari, Ali; Hanbay, Davut
    In this study, InceptionV3 based Enriched Feature Integration Network (Inc-EFIN) architecture was developed for automatic detection of surface defects. In the proposed architecture, features of all levels of the InceptionV3 architecture are extracted and the features with the same height and width are combined. As a result of merging, 5 feature maps were obtained. Channel-Based Squeeze and Excitation block has been applied to reveal important details in these feature maps. In Feature Pyramid Network module, information from low-level feature maps containing spatial details were transferred to high-level feature maps containing semantic details. Then, for the final feature map, features were combined using the Feature Integration and Signification (FIS) module. The feature map combined in the FIS module was passed through the Spatial and Channel-based Squeeze and Excitation block. Defect detection results were obtained by using convolution and sigmoid layers in the last layer of the Inc-EFIN architecture. MT, MVTec-Texture, and DAGM datasets were used to calculate the pixel-level defect detection success of the Inc-EFIN architecture. In experimental studies, Inc-EFIN architecture achieved higher performance than the latest technologies in the literature with 77.44% mIoU, 81.2% mIoU and 79.46% mIoU performance results in MT, MVTec-Texture and DAGM datasets, respectively.
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    Leaf-based plant species recognition based on improved local binary pattern and extreme learning machine
    (Elsevier, 2019) Turkoglu, Muammer; Hanbay, Davut
    Over the past 15 years, many feature extraction methods have been used and developed for the recognition of plant species. These methods have mostly been performed using separation operations from the background based on a pre-processing stage. However, the Local Binary Patterns (LBP) method, which provides high performance in object recognition, is used to obtain textural features from images without need for a pre-processing stage. In this paper, we propose different approaches based on LBP for the recognition of plant leaves using extracted texture features from plant leaves. While the original LBP converts color images to gray tones, the proposed methods are applied by using the R and G color channel of images. In addition, we evaluate the robustness of the proposed methods against noise such as salt & pepper and Gaussian. Later, the obtained features from the proposed methods were classified and tested using the Extreme Learning Machine (ELM) method. The experimental works were performed using various plant leaf datasets such as Flavia, Swedish, ICL, and Foliage. According to the obtained performance results, the calculated accuracy values for Flavia, Swedish, ICL and Foliage datasets were 98.94%, 99.46%, 83.71%, and 92.92%, respectively. The results demonstrate that the proposed method was more successful when compared to the original LBP, improved LBP methods, and other image descriptors for both noisy and noiseless images. (C) 2019 Elsevier B.V. All rights reserved
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    Maxillary sinus detection on cone beam computed tomography images using ResNet and Swin Transformer-based UNet
    (Elsevier Science Inc, 2024) Celebi, Adalet; Imak, Andac; Uzen, Huseyin; Budak, Umit; Turkoglu, Muammer; Hanbay, Davut; Sengur, Abdulkadir
    Objectives. This study, which uses artificial intelligence-based methods, aimed to determine the limits of pathologic conditions and infections related to the maxillary sinus in cone beam computed tomography (CBCT) images to facilitate the work of dentists. Methods. A new UNet architecture based on a state-of-the-art Swin transformer called Res-Swin-UNet was developed to detect the sinus. The encoder part of the proposed network model consists of a pre-trained ResNet architecture, and the decoder part consists of Swin transformer blocks. Swin transformers achieve powerful global context properties with self-attention mechanisms. Because the output of the Swin transformer generates sectorized features, the patch expanding layer was used in this section instead of the traditional upsampling layer. In the last layer of the decoder, sinus diagnosis was conducted through classical convolution and sigmoid function. In experimental works, we used a data set including 298 CBCT images. Results. The Res-Swin-UNet model achieved more success, with a 91.72% F1-score, 99% accuracy, and 84.71% IoU, outperforming the state-of-the-art models. Conclusions. The deep learning-based model proposed in the present study can assist dentists in automatically detecting the boundaries of pathologic conditions and infections within the maxillary sinus based on CBCT images. (Oral Surg Oral Med Oral
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    Multi-dimensional feature extraction-based deep encoder-decoder network for automatic surface defect detection
    (Springer London Ltd, 2023) Uzen, Huseyin; Turkoglu, Muammer; Hanbay, Davut
    The control of surface defects is of critical importance in manufacturing quality control systems. Today, automatic defects detection using imaging and deep learning algorithms has produced more successful results than manual inspections. Thanks to these automatic applications, manufacturing systems will increase the production quality, and thus financial losses will be prevented. However, since the appearance and dimensions of the defects on the surface are very variable, automatic surface defect detection is a complex problem. In this study, multi-dimensional feature extraction-based deep encoder-decoder network (MFE-DEDNet) network developed to solve such problems. An effective encoder-decoder model with lower parameters compared to the state-of-the-art methods is developed using the depthwise separable convolutions (DSC) layers in the proposed model. In addition, the 3D spectral and spatial features extract (3DFE) module of the proposed model is developed to extract deep spectral and spatial features, as well as deep semantic features. During the combination of these features, the multi-input attention gate (MIAG) module is used so that important details are not lost. As a result, the proposed MFE-DEDNet model based on these structures enabled the extraction of powerful and effective features for defect detection in surface datasets containing few images. In experimental studies, MVTec and MT datasets were used to evaluate the performance of the MFE-DEDNet. The experimental results achieved 80.01% and 56% mean intersection-over-union (mIoU) scores for the MT and MVTec datasets, respectively. In these results, it was observed that the proposed model produced higher success compared to other state-of-the-art methods.
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    A multi-division convolutional neural network-based plant identification system
    (Peerj Inc, 2021) Turkoglu, Muammer; Aslan, Muzaffer; Ari, Ali; Alcin, Zeynep Mine; Hanbay, Davut
    Background. Plants have an important place in the life of all living things. Today, there is a risk of extinction for many plant species due to climate change and its environmental impact. Therefore, researchers have conducted various studies with the aim of protecting the diversity of the planet's plant life. Generally, research in this area is aimed at determining plant species and diseases, with works predominantly based on plant images. Advances in deep learning techniques have provided very successful results in this field, and have become widely used in research studies to identify plant species. Methods. In this paper, a Multi-Division Convolutional Neural Network (MD-CNN)-based plant recognition system was developed in order to address an agricultural problem related to the classification of plant species. In the proposed system, we divide plant images into equal nxn-sized pieces, and then deep features are extracted for each piece using a Convolutional Neural Network (CNN). For each part of the obtained deep features, effective features are selected using the Principal Component Analysis (PCA) algorithm. Finally, the obtained effective features are combined and classification conducted using the Support Vector Machine (SVM) method. Results. In order to test the performance of the proposed deep-based system, eight different plant datasets were used: Flavia, Swedish, ICL, Foliage, Folio, Flowerl7, Flower102, and LeafSnap. According to the results of these experimental studies, 100% accuracy scores were achieved for the Flavia, Swedish, and Folio datasets, whilst the ICL, Foliage, Flower17, Flower102, and LeafSnap datasets achieved results of 99.77%, 99.93%, 97.87%, 98.03%, and 94.38%, respectively.
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    Multi-model LSTM-based convolutional neural networks for detection of apple diseases and pests
    (Springer Heidelberg, 2019) Turkoglu, Muammer; Hanbay, Davut; Sengur, Abdulkadir
    In this paper, we proposed Multi-model LSTM-based Pre-trained Convolutional Neural Networks (MLP-CNNs) as an ensemble majority voting classifier for the detection of plant diseases and pests. The proposed hybrid model is based on the combination of LSTM network with pre-trained CNN models. Specifically, in transfer learning, we adopted deep feature extraction from various fully connected layers of these pre-trained deep models. AlexNet, GoogleNet and DenseNet201 models are used in this work for feature extraction. The extracted deep features are then fed into the LSTM layer in order to construct a robust hybrid model for apple disease and pest detection. Later, the output predictions of three LSTM layers determined the class labels of the input images by majority voting classifier. In addition, we use an automatic scheme for determining the best choice of the network parameters of the LSTM layer. The experiments are carried out using data consisting of real-time apple disease and pest images from Turkey and the accuracy rates are calculated for performance evaluation. The experimental results show that by using the proposed ensemble combination structure, the results are comparable to, or better than, the pre-trained deep architectures.
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    A novel hybrid attention gate based on vision transformer for the detection of surface defects
    (Springer London Ltd, 2024) Uzen, Hueseyin; Turkoglu, Muammer; Ozturk, Dursun; Hanbay, Davut
    Many advanced models have been proposed for automatic surface defect inspection. Although CNN-based methods have achieved superior performance among these models, it is limited to extracting global semantic details due to the locality of the convolution operation. In addition, global semantic details can achieve high success for detecting surface defects. Recently, inspired by the success of Transformer, which has powerful abilities to model global semantic details with global self-attention mechanisms, some researchers have started to apply Transformer-based methods in many computer-vision challenges. However, as many researchers notice, transformers lose spatial details while extracting semantic features. To alleviate these problems, in this paper, a transformer-based Hybrid Attention Gate (HAG) model is proposed to extract both global semantic features and spatial features. The HAG model consists of Transformer (Trans), channel Squeeze-spatial Excitation (sSE), and merge process. The Trans model extracts global semantic features and the sSE extracts spatial features. The merge process which consists of different versions such as concat, add, max, and mul allows these two different models to be combined effectively. Finally, four versions based on HAG-Feature Fusion Network (HAG-FFN) were developed using the proposed HAG model for the detection of surface defects. The four different datasets were used to test the performance of the proposed HAG-FFN versions. In the experimental studies, the proposed model produced 83.83%, 79.34%, 76.53%, and 81.78% mIoU scores for MT, MVTec-Texture, DAGM, and AITEX datasets. These results show that the proposed HAGmax-FFN model provided better performance than the state-of-the-art models.
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    Plant disease and pest detection using deep learning-based features
    (Tubitak Scientific & Technological Research Council Turkey, 2019) Turkoglu, Muammer; Hanbay, Davut
    The timely and accurate diagnosis of plant diseases plays an important role in preventing the loss of productivity and loss or reduced quantity of agricultural products. In order to solve such problems, methods based on machine learning can be used. In recent years, deep learning, which is especially widely used in image processing, offers many new applications related to precision agriculture. In this study, we evaluated the performance results using different approaches of nine powerful architectures of deep neural networks for plant disease detection. Transfer learning and deep feature extraction methods are used, which adapt these deep learning models to the problem at hand. The utilized pretrained deep models are considered in the presented work for feature extraction and for further fine-tuning. The obtained features using deep feature extraction are then classified by support vector machine (SVM), extreme learning machine (ELM), and K-nearest neighbor (KNN) methods. The experiments are carried out using data consisting of real disease and pest images from Turkey. The accuracy, sensitivity, specificity, and F1-score are all calculated for performance evaluation. The evaluation results show that deep feature extraction and SVM/ELM classification produced better results than transfer learning. In addition, the fc6 layers of the AlexNet, VGG16, and VGG19 models produced better accuracy scores when compared to the other layers.
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    Plant Recognition System based on Deep Features and Color-LBP method
    (Ieee, 2019) Turkoglu, Muammer; Hanbay, Davut
    In recent years, deep learning, which is widely used in machine learning and computer vision, offers many new solutions, especially for agricultural problems. In this study, an approach based on the combination of Convolutional Neural Networks (CNN) and Color-Local Binary Pattern (C-LBP) method is recommended for the determination of plant species. Deep features have been obtained from the fc6 layer of the AlexNet model, a pre-trained ESA architecture. Then, LBP method is applied to each channel of color images (R, G, B). Finally, the deep features and LBP features from each color channel were combined and classified by Support Vector Machine (SVM). To test the accuracy of the proposed approach, ICL and Folio leaf data sets commonly used in the literature have been used. According to this results, accuracy rates of 98.50% and 99.48% were calculated for ICL and Folio data sets, respectively. The experimental results indicate that the proposed model achieves better accuracy compared to previous studies.
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    Plant recognition system based on extreme learning machine by using shearlet transform and new geometric features
    (Gazi Univ, Fac Engineering Architecture, 2019) Turkoglu, Muammer; Hanbay, Davut
    To date, different approaches have been used to be correctly identified of plant species. Leaves are the most important approaches as part of the plants which provide many features with advantages such as shape, color and vein texture. In this study, a new approach based on the geometrical properties of the leaf has been proposed. This method called Edge Step (ES), consists of features such as angle, center-edge length and edge distance by using edge points in the shape boundary curve. In addition, Shearlet Transform method, which has features such as good sensitivity to tissue identification, rapid calculation and directional independence, is used. In addition to these methods, Color features and Gray-Level Co-Occurrence Matrix (GLCM) method to extract color and texture properties from leaf images have been applied. Attributes derived from all these methods were tested with the Extreme Learning Machine (ELM) classifier method as separately and combination. The proposed study has been tested by using four different plant leaf datasets such as Flavia, Swedish, ICL and Foliage. Using these datasets, studies based on texture, shape and color characteristics have been compared with the performance of the proposed approach. As a result, the proposed method is identified to be more successful than the other methods.
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    PlantDiseaseNet: convolutional neural network ensemble for plant disease and pest detection
    (Springer London Ltd, 2022) Turkoglu, Muammer; Yanikoglu, Berrin; Hanbay, Davut
    Plant diseases and pests cause significant losses in agriculture, with economic, ecological and social implications. Therefore, early detection of plant diseases and pests via automated methods are very important. Recent machine learning-based studies have become popular in the solution of agricultural problems such as plant diseases. In this work, we present two classification models based on deep feature extraction from pre-trained convolutional neural networks. In the proposed models, we fine-tune and combine six state-of-the-art convolutional neural networks and evaluate them on the given problem both individually and as an ensemble. Finally, the performances of different combinations based on the proposed models are calculated using a support vector machine (SVM) classifier. In order to verify the validity of the proposed model, we collected Turkey-PlantDataset, consisting of unconstrained photographs of 15 kinds of disease and pest images observed in Turkey. According to the obtained performance results, the accuracy scores are calculated as 97.56% using the majority voting ensemble model and 96.83% using the early fusion ensemble model. The results demonstrate that the proposed models reach or exceed state-of-the-art results for this problem.
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    Recognition of plant leaves: An approach with hybrid features produced by dividing leaf images into two and four parts
    (Elsevier Science Inc, 2019) Turkoglu, Muammer; Hanbay, Davut
    Plants play a crucial role in the lives of all living things. A risk of extinction exists for many plants, hence many botanists and scientists are working in order to protect plants and plant diversity. Plant identification is the most important part of studies carried out for this purpose. In order to identify plants more accurately, different approaches have been used in the studies to date. One of these approaches is plant identification through leaf recognition, and is the basis of many conducted studies. It can be used for automatic plant recognition in the area of botany, the food sector, industry, medicine, and in many more areas too. In this study, image processing based on feature extraction methods such as color features, vein features, Fourier Descriptors (FD), and Gray-Level Co-occurrence Matrix (GLCM) methods are used. This study suggests the use of features extracted from leaves divided into two or four parts, instead of extracting for the whole leaf. Both the individual and combined performances of each feature extraction method are calculated by Extreme Learning Machines (ELM) classifier. The suggested approach has been applied to the Flavia leaf dataset. 10-fold cross-validation was used to evaluate the accuracy of the proposed method, which was then compared and tabulated with methods from other studies. The evaluated accuracy of the proposed method on the Flavia leaf dataset was calculated as 99.10%. (C) 2019 Elsevier Inc. All rights reserved.
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    Surface Defect Detection Using Deep U-Net Network Architectures
    (Ieee, 2021) Uzen, Huseyin; Turkoglu, Muammer; Hanbay, Davut
    Surface defects detection in products used in industry such as steel, fabric and marble is very important in terms of increasing product quality and preventing financial losses. However, automatic surface defects detection is a very difficult problem due to the complexity and diversity of surface defects. In this study, U-net based VGG16-Unet and Resnet34-Unet network models are proposed for Surface defects detection. The proposed model used spatial features in the first layers together with deep semantic features. In the proposed network models, the trained weights of the VGG16 and Resnet34 network architectures were used for the input parameters of the Unet architecture. In experimental studies, the highest F1-score value for MT and AITEX data sets was obtained as 91.07% and 94.67%, respectively, with the proposed Resnet34-Unet model. According to the results, it was observed that the defective areas showing similarity with the background were successfully separated by using the proposed model.
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    Swin-MFINet: Swin transformer based multi-feature integration network for detection of pixel-level surface defects
    (Pergamon-Elsevier Science Ltd, 2022) Uzen, Huseyin; Turkoglu, Muammer; Yanikoglu, Berrin; Hanbay, Davut
    Automatic surface defect detection is critical for manufacturing industries, such as steel, fabric, and marble industries. This study proposes a Swin transformer-based model called Multi-Feature Integration Network (Swin-MFINet) for pixel-level surface defect detection. The proposed model consists of an encoder, a Swin transformer-based decoder, and Multi-Feature Integration (MFI) modules. In the encoder module of the proposed model, a pre-trained Inception network is used to extract key features from small-size datasets. In the decoder section, global semantic features are obtained from the initial features by using the Swin-transformer block, which is the newest transformer technology of today. In addition, the convolution layer is used in the last step of the decoder, since transformers are limited in acquiring small spatial details such as edges, colors, and textures, which are important in detecting some small defects. In the last module called MFI, feature maps from different decoder stages are combined, and the channel squeeze-spatial excitation block is applied to reveal important features. Finally, a prediction map is obtained by applying a convolution layer and sigmoid activation function to the MFI module output, respectively. The performance of proposed model is analyzed over MT and MVTec datasets containing surface defect images. The proposed model obtained mIoU scores of 81.37%, and 77.07% respectively, for these two datasets These results outperform the state-of-the-art for the surface defect detection problem.
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    Texture defect classification with multiple pooling and filter ensemble based on deep neural network
    (Pergamon-Elsevier Science Ltd, 2021) Uzen, Huseyin; Turkoglu, Muammer; Hanbay, Davut
    Fabric quality control is one of the most important phases of production in order to ensure high-quality standards in the fabric production sector. For this reason, the development of successful automatic quality control systems has been a very important research subject. In this study, we propose a Multiple Pooling and Filter approach based on a Deep Neural Network (MPF-DNN) for the classification of texture defects. This model consists of three basic stages: preprocessing, feature extraction, and classification. In the preprocessing stage, the texture images were first divided into n x n equal parts. Then, median filtering and pooling processes were applied to each piece prior to performing image merging. In the proposed pre-treatment stage, it is aimed to clarify fabric errors and increase performance. For the feature extraction stage, deep features were extracted from the texture images using the pretrained ResNet101 model based on the transfer learning approach. Finally, classification and testing procedures were conducted on the obtained deep-effective properties using the SVM method. The multiclass TILDA dataset was used in order to test the proposed model. In experimental work, the MPF-DNN model for all four classes achieved a significant overall accuracy score of 95.82%. In the results obtained from extensive experimental studies, it was observed that the proposed MPF-DNN model was more successful than previous studies that used pretrained deep architectures.