A real-time defect detection algorithm for steel plate surfaces: the StarNet-GSConv-RetC3 detection transformer (SSR-DETR).

In view of the problems in industrial steel plate surface defect detection, such as high model complexity, insufficient recognition of small targets, and inefficient hardware deployment, this study proposes the StarNet-GSConv-RetC3 detection transformer (SSR-DETR) lightweight framework. The framework comprises a StarNet backbone network and an innovative star operation optimization structure to reduce computational complexity while enhancing feature representation capabilities. In the feature fusion stage, the RetBlock CSP bottleneck with 3 convolutions (RetC3) module with an explicit attenuation mechanism is designed to enhance the extraction of geometric features of microscopic defects by combining two-dimensional spatial priors, and grouped spatial convolution (GSConv) is used to optimize the aggregation of multiscale features. Experiments show that the algorithm achieves a mean average precision (mAP) of 88.2% and a classification accuracy of 92.0% on the Northeastern University steel surface defect (NEU-DET) dataset, which is 4.8% and 3.7% higher than the baseline model, respectively. Meanwhile, the model's computational load and size are reduced by 59.5% and 47.8%, respectively. Actual deployment tests show that this algorithm operates at 98.1 frames per second (FPS) on personal computer platforms and at 40.3 FPS, with a single-frame processing time of 24.8 ms, on the RK3568 embedded system, fully meeting the comprehensive requirements of industrial scenarios.