Leveraging Hierarchical Intensity Adjustment and Enhanced State-Space-Guided YOLO for Surface Defect Detection (relevant data)

The GC10-DET dataset is a real industrial surface defect dataset, comprising 3570 high-resolution (2048 x 1000) grayscale images with ten types of surface defects. These are Punching hole (Pu), Welding line (Wl), Crescent gap (Cg), Water spot (Ws), Oil spot (Os), Silk spot (Ss), Inclusion (In), Rolled pit (Rp), Crease (Cr), and Waist folding (Wf). These defects are naturally occurring and exhibit a variety of morphologies. After preprocessing the original dataset, the dataset used in the experiment consisted of 2470 samples, of which we used 2223 for training and 247 for model validation. Since some images may contain multiple defects, the actual number of instances for the training and validation sets is 3492 and 377, respectively. The GC10-DET dataset is available at https:\/\/github.com\/lvxiaoming2019\/GC10-DET-Metallic-Surface-Defect-Matasets@article{lv2020deep,    title={Deep metallic surface defect detection: The new benchmark and detection network},    author={Lv, Xiaoming and Duan, Fajie and Jiang, Jia-jia and Fu, Xiao and Gan, Lin},    journal={Sensors},    volume={20},    number={6},    pages={1562},    year={2020},    publisher={MDPI}}