PG-SalDETR: A Method for Detecting Small Defects in Steel Plates Based on Physically Guided Saliency and Asymmetric Perception Network

Steel plate defect detection is confronted with problems such as weak features of small defects, disconnection between physical priors and detection tasks, and semantic inconsistency of multi-scale fusion, which can easily lead to the misdetection of small defects. To solve these problems, this paper proposes a detection method named PG-SalDETR. Firstly, this paper proposes a physics-guided saliency perception mechanism (PGSPM), which transforms physical priors into learnable guidance signals and directly embeds them into the detection network for joint optimization. Secondly, this paper proposes the token sequence saliency perception network (TSSP-Net), which is designed to help improve the perception and representation of small defect features through an asymmetric dual-branch architecture, adaptive fusion, and residual fusion. Thirdly, a two-stage query refinement mechanism (TSQRM) is proposed. Through physically guided offset correction and adaptive multi-scale feature aggregation, it optimizes the query while preserving fine-grained defect details. Finally, the dynamic cross-scale fusion module (LCASF) is proposed. Through the dynamic cross-scale fusion strategy, the semantic inconsistency problem of small defect features in multi-scale fusion is alleviated. Experimental results demonstrate improvements. Compared to Salience DETR, PG-SalDETR achieves an AP increase of 3.8% and 2.6%, and an APS increase of 2.8% and 3.9% on the NEU-DET and GC10-DET datasets, respectively. These results indicate the effectiveness of the proposed method for small defect detection on steel plate surfaces.