Depth-wise Squeeze and Excitation Block-based Efficient-Unet model for surface defect detection

Üzen, Hüseyin; Türkoğlu, Muammer; Aslan, Muzaffer; Hanbay, Davut

doi:10.1007/s00371-022-02442-0

Cited by 40 publications

(9 citation statements)

References 77 publications

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“…Consequently, based on the correlation among channels, new weights were generated for them and exerted to the input matrices in turn [51]. By virtue of its cross-channel interaction capability, the SE block was able to selectively enhance the more significant features through learning global information [52]. In this case, global average pooling was applied to each channel of the input matrices and Swish and Sigmoid activation functions were utilized for the two one-dimension fully connected layers.…”

Section: Image Feature Learning Networkmentioning

confidence: 99%

A Tiny Object Detection Approach for Maize Cleaning Operations

Yu,

Li,

et al. 2023

Foods

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Real-time and accurate awareness of the grain situation proves beneficial for making targeted and dynamic adjustments to cleaning parameters and strategies, leading to efficient and effective removal of impurities with minimal losses. In this study, harvested maize was employed as the raw material, and a specialized object detection network focused on impurity-containing maize images was developed to determine the types and distribution of impurities during the cleaning operations. On the basis of the classic contribution Faster Region Convolutional Neural Network, EfficientNetB7 was introduced as the backbone of the feature learning network and a cross-stage feature integration mechanism was embedded to obtain the global features that contained multi-scale mappings. The spatial information and semantic descriptions of feature matrices from different hierarchies could be fused through continuous convolution and upsampling operations. At the same time, taking into account the geometric properties of the objects to be detected and combining the images’ resolution, the adaptive region proposal network (ARPN) was designed and utilized to generate candidate boxes with appropriate sizes for the detectors, which was beneficial to the capture and localization of tiny objects. The effectiveness of the proposed tiny object detection model and each improved component were validated through ablation experiments on the constructed RGB impurity-containing image datasets.

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Section: Image Feature Learning Networkmentioning

confidence: 99%

A Tiny Object Detection Approach for Maize Cleaning Operations

Yu,

Li,

et al. 2023

Foods

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“…With respect to the defect detection of deep learning-based pixel segmentation networks, this method not only realizes the functions of the above two methods, but also obtains the accurate shape and position information of the defects through the segmentation network, so as to achieve a reliable judgment of the surface quality. Uzen et al [19] proposed a novel method based on Depth-wise Squeeze and Excitation Block-based Efficient-Unet (DSEB-EUNet) for automatic surface defect detection. The proposed model includes a Unet network, and a deep extrusion and excitation block added to the skip connection of the Unet.…”

Section: Related Workmentioning

confidence: 99%

Study on the Sustainable Detection of Machining Surface Defects under the Influence of Environmental Interference

et al. 2023

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When detecting surface defects in the industrial cutting environment, the defects are easily contaminated and covered by many interference factors (such as chips and coolant residue) that exist on the machined surface. These interfering factors hinder the sustainable detection of surface defects. Furthermore, addressing the challenge of detecting surface defects in the presence of interference factors has proven to be a difficult problem in the current detection field. To solve this problem, a sustainable detection method for surface defects is proposed. The method is divided into two steps: one is the identification and removal of interference factors; the other is the detection of surface defects. First, a new FPN-DepResUnet model is constructed by modifying the Unet model from three aspects. The FPN-DepResUnet model is used to identify the interference factors in the image. Compared to the Unet model, the MAP of the FPN-DepResUnet model is increased by 5.77%, reaching 94.82%. The interfering factors are then removed using the RFR-net model. The RFR-net model performs point-to-point repair of interference regions. The repair process is performed by finding high-quality pixels similar to the interference region from the rest of the image. The negative effects of the interfering factors are removed by combining the FPN-DepResUnet model with the RFR-net model. On this basis, the SAM-Mask RCNN model is proposed for efficient defect detection of clean surface images. Compared with the Mask RCNN model, the MAP of the proposed SAM-Mask RCNN model increased by 2.00%, reaching 94.62%. Further, the inspection results can be fed back with a variety of surface defect information including defect types, the number of pixels in the different defect regions, and the proportion of different defect regions in the entire image. This enables predictive maintenance and control of the machined surface quality during machining.

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“…Unlike conventional convolution, DSC dissects the process into two stages: DC autonomously filters each input channel, and subsequently, PC amalgamates the DC outputs by means of a 1×1 convolution. In this regard, DSC can also be characterized as a factorized convolution approach [36][37][38]. Illustrated in Figure 1a, the utilization of DSC in a 2D context involves executing convolutional procedures separately for each channel of the input image via DC, thereby enabling the extraction of spatial attributes in individual dimensions.…”

Section: D/2d Dscnetmentioning

confidence: 99%

Multiscale Feature Fusion for Hyperspectral Image Classification Using Hybrid 3D-2D Depthwise Separable Convolution Networks

Firat,

Çiğ,

Güllüoğlu

et al. 2023

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Hyperspectral remote sensing images (HRSI) comprise three-dimensional image cubes, containing a single spectral dimension alongside two spatial dimensions. HRSI are presently among the foremost essential datasets for Earth observation. The task of HRSI classification is intricate due to the influence of spectral mixing, leading to notable variability within classes and resemblances across classes. Consequently, the field of HRSI classification has garnered significant research attention in recent times. Convolutional Neural Networks (CNNs) are harnessed to address these issues, enabling both feature extraction and classification. This study introduces a novel approach for HRSI classification called the hybrid 3D-2D depthwise separable convolution network (Hybrid DSCNet), which leverages multiscale feature integration. Within the Hybrid DSCNet, diverse kernel sizes contribute to an enriched feature extraction process from HRSI. The conventional 3D-2D CNN, while effective, comes with a computational load. Instead of using the standard 3D-2D CNN, this study adopts the 3D-2D DSC architecture. This approach partitions the conventional convolution into two components: pointwise and depthwise convolution, yielding a substantial reduction in trainable parameters and computational complexity. To evaluate the proposed method, the Indian Pines dataset along with WHU-Hi subdatasets (LongKou-LK, HanChuan-HC, and HongHu-HH) were employed. Employing a 5% training sample, impressive overall accuracy scores were achieved: 94.51%, 99.78%, 97.06%, and 97.27% for Indian Pines, WHU-LK, WHU-HC, and WHU-HH, respectively. Comparative analysis of the proposed approach with cutting-edge techniques within the literature reveals its superior performance across the four HRSI datasets. Notably, the Hybrid DSCNet attains enhanced classification accuracy while maintaining lower computational overhead.

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Depth-wise Squeeze and Excitation Block-based Efficient-Unet model for surface defect detection

Cited by 40 publications

References 77 publications

A Tiny Object Detection Approach for Maize Cleaning Operations

A Tiny Object Detection Approach for Maize Cleaning Operations

Study on the Sustainable Detection of Machining Surface Defects under the Influence of Environmental Interference

Multiscale Feature Fusion for Hyperspectral Image Classification Using Hybrid 3D-2D Depthwise Separable Convolution Networks

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