# Steel Surface Defect Detection Based on YOLOv8-TLC
## 2 Related Works  
Steel strips play an important role in various fields of industrial production, and their quality is crucial for industrial development. However, due to limitations in production processes and technology, defects such as cracks, inclusions, and spots often appear on the surface of steel. As a result, surface defect detection is particularly important. Currently, steel surface defect detection technology has evolved from traditional manual and machine-vision-based methods to deep learning-based techniques. Traditional manual inspection methods suffer from low accuracy, missed detections, and false positives, while machine-vision-based detection methods typically only extract shallow features of surface defects and are easily influenced by environmental factors, leading to unstable detection performance. Therefore, it is essential to employ deep learning-based object detection algorithms for detecting surface defects in steel strips. In 2022, [PERSON] et al. proposed the MSFT-YOLO [19] algorithm, which introduces the TRANS module based on YOLOv5 and combines a multi-scale feature fusion structure to achieve the integration of features at different scales, enhancing the detector's ability to dynamically adjust to targets of varying sizes. In 2023, [PERSON] [20] introduced the Self Notice Ratio (SNR) deep network attention model, combining it with YOLOv6 for surface defect detection in rolled steel. Their method inputs images processed by YOLOv6 into a U-Net convolutional model to confirm the presence of defects and accurately detect their positions. The final output is a binary mask that overlays the original image, achieving the detection goal. However, the detection time is relatively long, requiring further lightweight optimization. [PERSON] et al. (2019) did not directly use a lightweight convolutional network but instead designed the DSConv lightweight convolutional module to optimize the backbone of YOLOv4. [PERSON] (2019) proposed a novel approach for model lightweighting--a dual-stream network structure--dividing a single path into deep and shallow branches. The optimized YOLOv4 model had a parameter count reduced to 18.4 M, significantly lower than SSD and YOLOv4, successfully achieving model lightweighting. However, the model requires high image resolution, and detection speed may decrease at lower resolutions. [PERSON] (2019) proposed an improved method based on YOLOv5m, significantly enhancing the precision of steel surface defect detection, though the model's parameter count is relatively large. [PERSON] et al. developed an improved YOLOv5-based algorithm, creating a multi-scale block module ([PERSON] et al., 2019) that effectively explores steel surface defects at different resolutions. By 2024, [PERSON] et al. (2024) optimized YOLOv5 by introducing the ECA mechanism, which focuses on channel information while neglecting spatial information, thus being suboptimal for detection tasks. Their model achieved a mAP of 78.8% in steel surface defect detection, but the accuracy for detecting less visible defects was relatively low. [PERSON] et al. (2020), based on the introduction of the spatial attention (SA) mechanism, integrated the CSPCrossLayer module into the YOLOX backbone, establishing cross-layer connections. They also introduced the PSblock convolutional module to replace the CSPLayer structure in the feature fusion network. Experiments on the railway track dataset showed that this algorithm improved detection speed while maintaining accuracy, though the performance for small defect detection in the NEU-DET dataset was suboptimal. [PERSON] et al. (2020), to save computational resources, abandoned the FPN optimization of YOLOv7 and proposed a high-precision real-time defect detection algorithm, CDN-YOLOv7. This algorithm introduced the CARAFE lightweight upsampling operator to improve feature fusion ability, though CARAFE is more suited to scenarios requiring high-quality upsampling and devices with limited resources. [PERSON] et al.