Detection method for improving shape perception of small object defects on metal surfaces

Abstract

Defects on metal surfaces often exhibit complexity with diverse shapes, small sizes, and irregular patterns, leading to frequent missed and false detections during inspection and posing significant challenges to automated detection systems. Existing advanced object detectors, when applied directly to small defect detection on metal surfaces, fail to achieve satisfactory results. To mitigate these issues, we proposed a detection method to enhance the shape perception of small object defects on metal surfaces, namely MetalYOLO. Firstly, a novel location-aware attention mechanism is designed to integrate deformable convolutions to form a new feature selection module to enhance the focus on key defect features, optimizes the generation of offsets, and improve the model’s ability to adapt to complex shape objects. Secondly, the standard up-sampling module is replaced with a dynamic sampling module to dynamically adjust the sampling pattern of the input feature distribution to improve computational efficiency and retain complex or small-scale object features, thereby improving detection accuracy. Finally, a new detail-enhanced detection head is designed to further improve the network’s ability to capture fine-grained details by introducing a detail-enhanced attention-sharing module so as to utilize contextual information to selectively suppress irrelevant features, thereby reducing information redundancy. The proposed model is compared with baseline models on the ILS-MB and NEU-DET datasets. and the experimental results show significant improvements in false detection and missed detection rates with only a slight loss in inference speed. Meanwhile, the mAP reached 80.4% and 79.0%, respectively, which is 1.7% and 3.2% higher than the baseline algorithm.