Hyper-Progressive Real-Time Detection Transformer (HPRT-DETR) algorithm for defect detection on metal bipolar plates

Abstract

Aiming to address common defects such as scratches, cracks, bumps, and indentations on the surface of metal bipolar plates, this thesis proposes an algorithm called HPRT-DETR for detecting defects on metal bipolar plates. The algorithm aims to address issues such as small defects, complex backgrounds, and low detection accuracy. To enhance the performance of the algorithm, we adopt the DA to improve the AIFI module. This enhancement enables the algorithm to focus on the defective region, helping it capture more informative features. Meanwhile, we have implemented Zoom-cat scaling splicing and SSF to enhance the multi-scale feature fusion capability of the network in the CCFM module. Additionally, we have introduced the NWD metric loss to reduce sensitivity to small target locations, thereby improving detection accuracy and efficiency. Experimental validation shows that the enhanced HPRT-DETR model achieves improvements of 6.4, 1.7, and 4.7 percentage points in accuracy, recall, and average precision, respectively, compared to the original model. These results indicate that the enhanced model lays the foundation for automated production and intelligent inspection of metal bipolar plates.