Yazar "Aslan, Muzaffer" seçeneğine göre listele

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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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    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.