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Öğe 3 Boyutlu Evrişimsel Sinir Ağı Kullanılarak Hiperspektral Görüntülerin Sınıflandırılması(2022) Hanbay, Davut; Fırat, HüseyinHiperspektral görüntü sınıflandırma, uzaktan algılanan görüntülerin analizi için yaygın olarak kullanılmaktadır. Bir hiperspektral görüntü, uygulamalarda büyük potansiyele sahip olan yer nesnelerinin zengin spektral bilgilerini ve uzamsal bilgilerini içermektedir. Spektral uzamsal bilgi kullanımı hiperspektral görüntü sınıflandırmasının performansını önemli ölçüde arttırmaktadır. Hiperspektral görüntüler, 3B küpler biçiminde gösterilmektedir. Bu nedenle, 3B uzamsal filtreleme, bu tür görüntülerdeki spektral uzamsal özellikleri eşzamanlı olarak çıkarmak için doğal olarak basit ve etkili bir yöntem sunmaktadır. Bu çalışmada, hiperspektral görüntü sınıflandırması için bir 3B evrişimli sinir ağı (3B ESA) yöntemi önerilmiştir. Önerilen yöntem, derin spektral uzamsal birleştirilmiş özellikleri etkin bir şekilde çıkarmaktadır. Aynı zamanda herhangi bir ön işleme veya son işleme dayanmadan hiperspektral görüntü küpü verileri toplu olarak görüntülemektedir. Hiperspektral görüntü küpü önce küçük üst üste binen 3B parçalara bölünmektedir. Daha sonra bu parçalar, spektral bilgileri de koruyan birden çok bitişik bant üzerinde bir 3B çekirdek işlevi kullanarak 3B özellik haritaları oluşturmak için işlenmektedir. Önerilen yöntem indian pines, pavia üniversitesi ve botswana veri setleri ile test edilmiştir. Deneysel çalışmalar sonucunda, indian pines için %99,35, pavia üniversitesi için %99,90 ve botswana için ise %99,59 genel doğruluk sonuçları elde edilmiştir. Sonuçlar, 4 farklı derin öğrenme tabanlı yöntemle karşılaştırılmıştır. Deneysel sonuçlardan, önerilen 3B ESA yöntemimizin daha iyi performans gösterdiği görülmektedir.Öğe 3D residual spatial-spectral convolution network for hyperspectral remote sensing image classification(Springer London Ltd, 2023) Firat, Huseyin; Asker, Mehmet Emin; Bayindir, Mehmet Ilyas; Hanbay, DavutHyperspectral remote sensing images (HRSI) are 3D image cubes that contain hundreds of spectral bands and have two spatial dimensions and one spectral dimension. HRSI analysis are commonly used in a wide variety of applications such as object detection, precision agriculture and mining. HRSI classification purposes to assign each pixel in HRSI to a unique class. Deep learning is seen as an effective method to improve HRSI classification. In particular, convolutional neural networks (CNNs) are increasingly used in remote sensing field. In this study, a hybrid 3D residual spatial-spectral convolution network (3D-RSSCN) is proposed to extract deep spatiospectral features using 3D CNN and ResNet18 architecture. Simultaneously spatiospectral features extraction is provided using 3D CNN. In deeper CNNs, ResNet architecture is used to achieve higher classification performance as the number of layers increases. In addition, thanks to the ResNet architecture, problems such as degradation and vanishing gradient that may occur in deep networks are overcome. The high dimensionality of the HRSIs increases the computational complexity. Thus, most of studies apply dimension reduction as preprocessing. In the proposed study, principal component analysis (PCA) is used as the preprocessing step for optimum spectral band extraction. The proposed 3D-RSSCN method is tested with Indian pines, Pavia University and Salinas datasets and compared against various deep learning-based methods (SAE, RPNet, 2D CNN, 3D CNN, M3D CNN, HybridSN, FC3D CNN, SSRN, FuSENet, S3EResBoF). As a result of the applications, the best classification accuracy among these methods compared in all datasets is obtained with the proposed 3D-RSSCN. The proposed 3D-RSSCN method has the best accuracy and time performance in classifying.Öğe 4CF-Net: Hiperspektral uzaktan algılama görüntülerinin spektral uzamsal sınıflandırılması için yeni 3B evrişimli sinir ağı(2022) Fırat, Hüseyin; Hanbay, DavutHiperspektral görüntüler (HG), uzaktan algılamada yaygın olarak kullanılan bitişik bant görüntüleridir. Derin öğrenme HG sınıflandırmasında kullanılan etkili bir yöntemdir. Evrişimli sinir ağları (ESA) ise HG sınıflandırmasında kullanılan derin öğrenme yöntemlerinden biridir. Spektral-uzamsal alanlardan HG’lerin soyut özelliklerini öğrenebilen otomatik yaklaşımlar sağlamaktadır. HG’lerin yüksek boyutsallığı hesaplama karmaşıklığını arttırmaktadır. Bu nedenle, geliştirilen ESA modellerinin çoğu, ön-işleme adımı olarak boyut indirgeme gerçekleştirmektedir. HG sınıflandırmasındaki diğer bir problem ise, doğru sonuçlar elde etmek için spektral-uzamsal özelliklerin dikkate alınması gerekliliğidir. Çünkü, HG sınıflandırma performansı büyük ölçüde spektral-uzamsal bilgilere bağlıdır. Bu çalışmada, HG sınıflandırması için yeni bir 3B ESA modeli önerilmiştir. Önerilen yöntem, HG’lerdeki spektral-uzamsal özellikleri eşzamanlı olarak çıkarmak için etkili bir yöntem sağlamaktadır. Ağ, girişte 3B hiperspektral küpü kullanmaktadır. Hiperspektral küpteki boyutsal fazlalığı gidermek için temel bileşen analizi kullanılmaktadır. Daha sonra komşuluk çıkarımı kullanılarak, spektral-uzamsal özellikler etkin bir şekilde çıkarılmaktadır. Önerilen yöntem 4 veriseti ile test edilmiştir. Uygulama sonuçları 7 farklı derin öğrenme tabanlı yöntemle karşılaştırılmış ve 4CF-Net yöntemimizin daha iyi sınıflandırma performansı gösterdiği görülmüştür.Öğe Apricot Disease Identification based on Attributes Obtained from Deep Learning Algorithms(Ieee, 2018) Turkoglu, Muammer; Hanbay, DavutIn 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.Öğe Automatic Thresholding Method Developed With Entropy For Fabric Defect Detection(Ieee, 2019) Uzen, Huseyin; Firat, Huseyin; Karci, Ali; Hanbay, DavutFabric defect detection is one of the most important areas for quality control of products in the textile industry. Many different studies have developed methods for this problem. In this study, an automatic thresholding method developed with entropy has been proposed. Due to the low cost of calculation, the proposed automatic thresholding method will be very suitable for real-time applications. In this study, automatic thresholding method which is supported by 4 different entropy method was compared with otsu method which is one of automatic thresholding methods. Various tests have been made on different fabric types for comparisons. As a result of experimental studies, successful results of automatic thresholding methods supported with entropy were obtained for fabric defect detection. Renyi entropy method was the most successful result among the proposed methods.Öğe Bölgesel evrişimsel sinir ağları tabanlı MR görüntülerinde tümör tespiti(2019) Arı, Ali; Hanbay, DavutÖz: Beyin tümörlerinden kaynaklı insan ölümleri günümüzde artmaktadır. Beyin tümörü çok hızlı büyüyerek, normal boyutunun iki katına çıkabilir. Bu yüzden uzmanlar, Manyetik Rezonans (MR) görüntülerini inceleme sürecini dikkatli ve hızlı bir şekilde yapmalıdır. Erken teşhis, kanser tanısında, tedavi planlamasında ve tedavi sonucunun değerlendirilmesinde hayati öneme sahiptir. Eğer beyin tümörü olan bir hasta doğru ve erken tedavi görmemişse, hastanın hayatta kalma şansı düşebilir ve ölümle sonuçlanabilir. Bu makalede, beyin MR görüntülerinden tümörü kolayca tespit eden ve tümörün yerini belirten, uzmanlara yardımcı olabilecek bilgisayar destekli otomatik tümör tespit sistemi geliştirilmiştir. Geliştirilen sistem derin öğrenme mimarilerinden olan Bölgesel tabanlı Evrişimsel Sinir Ağları (BESA) tabanlıdır. BESA, Evrişimsel Sinir Ağları (ESA) mimarisini kullanan bir yapı olmakla birlikte giriş görüntüsüne ek olarak ilgilenilen bölgenin de giriş olarak verildiği bir yapı olarak düşünülebilir. Farklı BESA mimarileri tasarlanarak Benchmark, Rembredant ve Harvard veri setleri üzerinde test edilmiştir. Elde edilen en yüksek doğruluk değeri %99,10 ile BESA4 mimarisi ve Benchmark veri setinden elde edilmiştir. En yüksek ortalama doğruluk ise yine BESA4 mimarisi ile %98,66 olarak hesaplanmıştır. Ayrıca, önerilen yöntemin başarımı, literatürdeki bazı yöntemler ile kıyaslanmıştır. Kıyaslamalar da önerilen yöntemin daha başarılı olduğu görülmüştür.Öğe Classification of Breast Masses in Mammogram Images using KNN(Ieee, 2015) Alpaslan, Nuh; Kara, Asuman; Zencir, Busra; Hanbay, DavutBreast cancer is one of the most deadly diseases for women. Mammogram is very important imaging tecnique used diagnosis in early stages of breast cancer. In this study, a decision support system which helps experts to examine mammogram images in the fight against breast cancer is developed. In this study, firstly several preprocesses are applied to mammogram to make image clear and segmentation of mass is provided with an appropriate threshold value. After the segmentation processes, features of the tumor mass are obtained. The obtained features are classified as normal, benign or malignant using kNN (k-nearest neighbours) classifiers. In this study, its have been were shown that, effect of kurtosis, skewness and wavelet energy features on classification performance is shown. As a result, it has been seen that, these features improve the classification performance.Öğe Classification of EMG Signals by LRF-ELM(Ieee, 2017) Ayaz, Furkan; Ari, Ali; Hanbay, DavutElectromyogram (EMG) signal can be defined as the electrical activity of muscles cells. It is commonly used in motion recognition, treatment of neuromuscular disorders and prosthetic hand control. In this study, classification of EMG signals obtained from 6 different hand shapes of holding object was proposed. At first Short Time Fourier Transform of the EMG signal were evaluated to obtain their Time-Frekans representation. After than these T-F images were segmented and their mean values were evaluated to reduce the dimension of the images. Local Receptive Fields based Extreme Learning Machines (ELM-LRF) used to classification of these hand shapes of holding object. Evaluated accuracy is 94.12 %.Öğe Classification of hyperspectral remote sensing images using different dimension reduction methods with 3D/2D CNN(Elsevier, 2022) Firat, Huseyin; Asker, Mehmet Emin; Hanbay, DavutThe high dimensionality of hyperspectral remote sensing images (HRSI) affects the classification performance. Therefore, most HRSI classification methods use dimension reduction methods as a solution for high dimensionality. It is aimed to extract useful features with dimension reduction methods. At the end of this process, the data dimension is reduced and the transaction cost is decreased. In this study, LDA, PCA, IPCA, ICA, SPCA, RPCA and SVD dimension reduction methods were applied as a preprocessing step to improve HRSI classification performance. Since HRSI is volumetric data and has a spectral dimension, 2D CNN cannot extract good distinguishing features from spectral dimensions. Because 2D CNN only considers spatial information. With 3D CNN, spectral-spatial features are extracted simultaneously. However, 3D CNN increases the computational cost. Therefore, in this study, Hybrid 3D/2D CNN method is used together with dimension reduction methods. Hybrid CNN method consists of a combination of 3D CNN, 2D CNN and depthwise separable convolution. While 3D CNN extracts common spectral-spatial features, more spatial features are learned with 2D CNN used after 3D CNN. With depthwise separable convolution, it reduces the number of parameters and overfitting is prevented. The applications performed on the frequently used HRSI benchmark datasets show that the classification performance of the proposed method is better than the compared methods. In addition, Indian pines, HyRANK-Loukia, Botswana and Pavia of University datasets are used to examine the effect of dimension reduction methods used together with the hybrid 3D/2D CNN method on classification performance. As a result of the applications, the best classification accuracies were obtained in PCA, LDA and IPCA with Indian pines, PCA with Pavia of university, PCA and IPCA with Salinas, PCA, RPCA and LDA dimension reduction methods with HyRANK-Loukia.Öğe Classification of The Grape Varieties based on Leaf Recognition by Using SVM Classifier(Ieee, 2015) Turkoglu, Muammer; Hanbay, DavutIn 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%.Öğe A CNN based real-time eye tracker for web mining applications(Springer, 2022) Donuk, Kenan; Ari, Ali; Hanbay, DavutEye gaze tracking is an increasingly important technology in the field of human-computer interaction. Individuals' preferences, tendencies, and attention can be measured by processing the data obtained from face and eye images. This technology is used in advertising, market research, web page design, education, learning methods, and various neurological-psychiatric studies of medical research. Many different methods have been used in eye gaze tracking tasks. Today, commonly model-shape and appearance-based methods are used. Model-shape based methods require less workload than appearance-based methods. But it is more sensitive to environmental conditions. Appearance-based methods require powerful hardware, but they are less susceptible to environmental conditions. Developments in technology have paved the way for applying appearance-based models in eye gaze tracking. In this paper, a CNN-based real-time eye tracking system was designed to overcome environmental problems in eye gaze tracking. The designed system is used to determine the areas of interest of the user in web pages. The performance of the designed CNN-based system is evaluated during the training and testing phases. In the training phase, the difference between the desired and determined points on the screen is 32 pixels and in testing phase, the difference between the desired and determined points on the screen is 53 pixels. The results of the test trials have shown that the proposed system could be used successfully in eye tracking studies on web pages.Öğe Combination of Deep Features and KNN Algorithm for Classification of Leaf-Based Plant Species(Ieee, 2019) Turkoglu, Muammer; Hanbay, DavutRecently, 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.Öğe Comparison of 3D CNN based deep learning architectures using hyperspectral images(Gazi Univ, Fac Engineering Architecture, 2023) Firat, Huseyin; Hanbay, DavutHyperspectral images (HSI) are 3-dimensional (3D) image cubes with two spatial and one spectral dimensions. The development of deep learning methods has had a significant impact on HSI classification. Especially convolutional neural network (CNN) based methods are getting more attention in this field. In this study, we make use of the deep learning architectures LeNet5, AlexNet, VGG16, GoogleNet and ResNet50, which are among the successful examples of CNN for the HSI classification problem. We use a 3D CNN-based hybrid approach when using these architectures. Because, using 3D CNN, spectral-spatial features are extracted simultaneously. In this case, the classification accuracy of HSIs is increased with the spectral-spatial-based deep learning architecture. However, in the proposed model, principal component analysis (PCA) is used as a preprocessing technique for optimal band extraction from HSIs. After applying PCA, 3D cubes are obtained by neighborhood extraction and given to the input of deep learning architectures. Indian pines, Salinas, Botswana and HyRANK-Loukia datasets were used to compare the classification performances of 3D CNN-based deep learning architectures. As a result of the applications, the best classification accuracy was obtained with VGG16 architectures in Indian pines dataset, ResNet50 in Botswana dataset, VGG16 in HyRANK-Loukia dataset, LeNet5 and VGG16 architectures in Salinas dataset.Öğe Comparison of intelligent methods for predicting aeration efficiency of high-head conduits(Academic Publication Council, 2012) Unsal, Mehmet; Hanbay, DavutThe ecological quality of water depends largely on amount of oxygen that the water can hold. Oxygen enters water by entrainment of air bubbles. There is a significant oxygen transfer associated with most hydraulic structures because the air entrained into the flow is split into small bubbles, which greatly increase the surface area for transfer. To design efficient hydraulic structures they must be modeled and analyzed correctly before they are realized. Different methods based on mathematical, statistical and intelligent methods are used for modeling and analyzing. In this paper, comparison of intelligent methods for predicting aeration efficiency of high-head conduits was presented. The intelligent methods used were Neural Network (NN), Adaptive Network based Fuzzy Inference Systems (ANFIS) and Least Squares Support Vector Machines (LS-SVM). The 3-k cross validation test was applied to evaluate the performance of intelligent methods. The predicted values were compared with the experimental measured values and R-2 statistics were calculated and tabulated. All methods have good agreement with experimental results. According to calculated statistics, the best performance was obtained with the LS-SVM model at R-2 0.9815.Öğe Computer-aided Tumor Detection System Using Brain MR Images(Ieee, 2015) Ari, Ali; Alpaslan, Nuh; Hanbay, DavutIn today's technology, computer assisted detection applications have managed to make great contributions to the field of medicine. Computer assisted detection systems aim to help radiologist about mass detection by using image processing systems. In this study, it's aimed to carry out mass detection process on the images of 3D brain MRI (Magnetic Resonance Imaging). The steps followed in this study are the stage of pre-processing the stage of segmentation, identification of the areas of interests and identification of tumor. As a result of processing's in the stages of preprocessing and segmentation, obtained areas of interests are labelled and attributes of these areas of interests are extracted during the stage of attributes extraction and in the last stage, the areas of interests are identified as whether they are mass or not according to these attributes. With this method applied on 845 number of magnetic resonance image sections belonging to 13 patients, it has been achieved classification success with 86.39%.Öğe Continuous rotation invariant features for gradient-based texture classification(Academic Press Inc Elsevier Science, 2015) Hanbay, Kazim; Alpaslan, Nuh; Talu, Muhammed Fatih; Hanbay, Davut; Karci, Ali; Kocamaz, Adnan FatihExtracting rotation invariant features is a valuable technique for the effective classification of rotation invariant texture. The Histograms of Oriented Gradients (HOG) algorithm has been proved to be theoretically simple, and has been applied in many areas. Also, the co-occurrence HOG (CoHOG) algorithm provides a unified description including both statistical and differential properties of a texture patch. However, HOG and CoHOG have some shortcomings: they discard some important texture information and are not invariant to rotation. In this paper, based on the original HOG and CoHOG algorithms, four novel feature extraction methods are proposed. The first method uses Gaussian derivative filters named GDF-HOG. The second and the third methods use eigenvalues of the Hessian matrix named Eig(Hess)-HOG and Eig(Hess)-CoHOG, respectively. The fourth method exploits the Gaussian and means curvatures to calculate curvatures of the image surface named GM-CoHOG. We have empirically shown that the proposed novel extended HOG and CoHOG methods provide useful information for rotation invariance. The classification results are compared with original HOG and CoHOG algorithms methods on the CUReT, KTH-TIPS, KTH-TIPS2-a and UIUC datasets show that proposed four methods achieve best classification result on all datasets. In addition, we make a comparison with several well-known descriptors. The experiments of rotation invariant analysis are carried out on the Brodatz dataset, and promising results are obtained from those experiments. (C) 2014 Elsevier Inc. All rights reserved.Öğe A deep feature extractor approach for the recognition of pollen-bearing bees(Ieee, 2020) Turkoglu, Muammer; Uzen, Huseyin; Hanbay, DavutIn 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.Öğe Deep Feature Selection for Facial Emotion Recognition Based on BPSO and SVM(Gazi Univ, 2023) Donuk, Kenan; Ari, Ali; Ozdemir, Mehmet Fatih; Hanbay, DavutFacial expressions, which are important social communication tools in our daily life, provide important information about the mental state of people. Research is being done to obtain this information accurately. The importance of these researchs in the field of human-computer interaction is increasing. Many methods have been used for the recognition of universal facial expressions such as neutral, happiness, surprise, sadness, anger, disgust, and fear by intelligent systems with high accuracy. Emotion recognition is an example of difficult classification due to factors such as ambient light, age, race, gender, and facial position. In this article, a 3-stage system is proposed for emotion detection from facial images. In the first stage, the CNN-based network is trained with the Fer+ dataset. The Binary Particle Swarm Optimization algorithm is applied for feature selection to the feature vector in the fully connected layer of the CNN network trained in the second stage. Selected features are classified by Support Vector Machine. The performance of the proposed system has been tested with the Fer+ dataset. As a result of the test, 85.74% accuracy was measured. The results show that the combination of BPSO and SVM contributes to the classification accuracy and speed of the FER+ dataset.Öğe Deep learning based brain tumor classification and detection system(TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, ATATURK BULVARI NO 221, KAVAKLIDERE, ANKARA, 00000, TURKEY, 2018) Arı, Ali; Hanbay, DavutThe brain cancer treatment process depends on the physician's experience and knowledge. For this reason, using an automated tumor detection system is extremely important to aid radiologists and physicians to detect brain tumors. The proposed method has three stages, which are preprocessing, the extreme learning machine local receptive fields (ELM-LRF) based tumor classification, and image processing based tumor region extraction. At first, nonlocal means and local smoothing methods were used to remove possible noises. In the second stage, cranial magnetic resonance (MR) images were classified as benign or malignant by using ELM-LRF. In the third stage, the tumors were segmented. The purpose of the study was using only cranial MR images, which have a mass, in order to save the physician's time. In the experimental studies the classification accuracy of cranial MR images is 97.18%. Evaluated results showed that the proposed method's performance was better than the other recent studies in the literature. Experimental results also proved that the proposed method is effective and can be used in computer aided brain tumor detection.Öğe Depth-wise Squeeze and Excitation Block-based Efficient-Unet model for surface defect detection(Springer, 2023) Uzen, Huseyin; Turkoglu, Muammer; Aslan, Muzaffer; Hanbay, DavutDetection 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.
