Defect detection on the surface of the steel strip is essential for the quality assurance of the steel strip. Precise localization and classification, the two significant tasks of defect detection, still need to be completed due to the diversity of defect scales. In this paper, a residual atrous spatial pyramid pooling (RASPP) module is first designed to enrich the multi-scale information of the feature maps and increase the receptive field of the feature maps. Secondly, a double pyramid network (DPN) that combines RASPP and feature pyramid is proposed to fuse multi-scale features further so that similar semantic features are shared among the features of each layer. Finally, DPN-Detector, an automatic surface defects detection network, is proposed, which embeds the DPN module into Faster R-CNN and replaces the original detection head with a designed double head. Experiments are carried out on the steel strip surface defect dataset (NEU-DET), and the results show that the mAP of DPN-Detector is as high as 80.93%, which is 3.52% higher than that of the baseline network Faster R-CNN. The classification accuracy is 74.64%, and the detection speed reaches 18.62 FPS. The proposed method performs better robustness, classification and regression capability than other steel strip defect detection methods.

Photovoltaic panel defect detection presents significant challenges due to the wide range of defect scales, diverse defect types, and severe background interference, often leading to a high rate of false positives and missed detections. To address these challenges, this paper proposes the LEM-Detector, an efficient end-to-end photovoltaic panel defect detector based on the transformer architecture. To address the low detection accuracy for Crack and Star crack defects and the imbalanced dataset, a novel data augmentation method, the Linear Feature Augmentation (LFA) module, specifically designed for linear features, is introduced. LFA effectively improves model training performance and robustness. Furthermore, the Efficient Feature Enhancement Module (EFEM) is presented to enhance the receptive field, suppress redundant information, and emphasize meaningful features. To handle defects of varying scales, complementary semantic information from different feature layers is leveraged for enhanced feature fusion. A Multi-Scale Multi-Feature Pyramid Network (MMFPN) is employed to selectively aggregate boundary and category information, thereby improving the accuracy of multi-scale target recognition. Experimental results on a large-scale photovoltaic panel dataset demonstrate that the LEM-Detector achieves a detection accuracy of 94.7% for multi-scale defects, outperforming several state-of-the-art methods. This approach effectively addresses the challenges of photovoltaic panel defect detection, paving the way for more reliable and accurate defect identification systems. This research will contribute to the automatic detection of surface defects in industrial production, ultimately enhancing production efficiency.