Study on end-to-end detection method for surface defects of automotive sheet metal parts

Sheet metal parts account for more than 60% of the total automotive parts, and their defects can seriously affect the safety of automobile operations. Therefore, it is very important to detect defects in sheet metal parts during the production process. Due to the small size of defects in sheet metal parts, and high detection precision required, the traditional detection method cannot meet the requirements. And the factory production speed is fast, if the detection speed is low, it will cause defects to escape. Therefore, we propose an end-to-end detection method for automotive sheet metal parts surface defects. To effectively improve the detection speed, the dual regression classification strategy is proposed, which removes the NMS post-processing. Gradient information branch is added to provide rich gradient information for the model and mitigate the information loss during long convolution. Use the SPD-Conv module, optimized for small-size defects detection, to retain complete space information. Finally, the model is evaluated on the automotive sheet metal parts defect dataset. The experimental results show that the proposed method is superior to the benchmark methods in precision and speed, with mAP of 92.32% and FPS of 39.06, which achieves end-to-end detection.