Surface defect segmentation supports real-time surface defect detection system of steel sheet by reducing redundant information and highlighting the critical defect regions for high-level image understanding. Existing defect segmentation methods usually lack adaptiveness to different shape, size and scale of the defect object. Based on the observation that the defective area can be regarded as the salient part of image, a saliency detection model using double low-rank and sparse decomposition (DLRSD) is proposed for surface defect segmentation. The proposed method adopts a low-rank assumption which characterizes the defective sub-regions and defect-free background sub-regions respectively. In addition, DLRSD model uses sparse constrains for background sub-regions so as to improve the robustness to noise and uneven illumination simultaneously. Then the Laplacian regularization among spatially adjacent sub-regions is incorporated into the DLRSD model in order to uniformly highlight the defect object. Our proposed DLRSD-based segmentation method consists of three steps: firstly, using DLRSD model to obtain the defect foreground image; then, enhancing the foreground image to establish the good foundation for segmentation; finally, the Otsu’s method is used to choose an optimal threshold automatically for segmentation. Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches in terms of both subjective and objective tests. Meanwhile, the proposed method is applicable to industrial detection with limited computational resources.

During the production process of steel plate, surface defect detection is crucial for high-quality products. For the existing defect detection method based on machine vision, there are various types of problems, such as large model calculations, low detection accuracy and difficulties of recognizing small defect targets. To reduce and solve these issues, the paper proposes a new defect detection model, simplified kernel and squeeze on a you only look once network (SKS-YOLO), which can achieve rapid and effective defect detection on steel plate. Firstly, it adopts EfficientNetv2 as the backbone, significantly reducing model calculations and accelerating training speed while maintaining accuracy. Subsequently, the atrous spatial pyramid pooling (ASPP) module is utilized to obtain a larger receptive field, extracting more feature information from surface defects. The integration of the squeeze excitation network (SE-Net) attention mechanism enhances capabilities of feature extraction furtherly. Then, the K-means algorithm is applied to cluster and obtain more suitable anchor frames for defect targets. It not only increases the number of positive samples, but also expedites model convergence. Finally, the loss function of simplified intersection over union (SIoU) is used to enhance the ability of model to locate and detect surface defect targets. The experimental results show that the mean average precision (mAP) is 89.40% at a detection speed of 55 frames per second (FPS), which is better than the state-of-the-art (SOTA) detection models.

The defects of solar cell component (SCC) will affect the service life and power generation efficiency. In this paper, the defect images of SCC were taken by the photoluminescence (PL) method and processed by an advanced lightweight convolutional neural network (CNN). Firstly, in order to solve the high pixel SCC image detection, each silicon wafer image was segmented based on local difference extremum of edge projection (LDEEP). Secondly, in order to detect the defects with small size or weak edges in the silicon wafer, an improved lightweight CNN model with deep backbone feature extraction network structure was proposed, as the enhancing feature fusion layer and the three-scale feature prediction layer; the model provided more feature detail. The final experimental results showed that the improved model achieves a good balance between the detection accuracy and detection speed, with the mean average precision (mAP) reaching 87.55%, which was 6.78% higher than the original algorithm. Moreover, the detection speed reached 40 frames per second (fps), which meets requirements of precision and real-time detection. The detection method can better complete the defect detection task of SCC, which lays the foundation for automatic detection of SCC defects.

The defects in photovoltaic cells affect the service life and power generation efficiency of the entire photovoltaic system. Aiming at the high missed detection rate of weak and small defects in the automatic detection of existing cells, a feature-enhanced lightweight convolutional neural network model was established. The feature enhancement extraction module was designed specifically to improve the extraction ability of weak boundaries. In addition, according to the principle of multi-scale recognition, a small target prediction layer was added to realize multi-scale feature prediction. In the experimental test, the mean average precision (mAP) of the model reaches to 87.55%, which is 6.78 percentage points higher than the traditional model. Moreover, the detection speed reaches to 40 fps, which meets the accuracy and real-time detection requirements.