Class Knowledge-Guided Lightweight Network for Salient Object Detection of Strip Steel Surface Defect

Recent lightweight salient object detection (SOD) models for strip steel surface defect typically adopt classification model as encoder to extract semantic features, followed by task-specific decoder for defect detection. Despite achieving promising results, these models often overlook the potential of leveraging class-level knowledge to enhance detection performance. In this paper, we propose a Class Knowledge-Guided Lightweight Network (CKLNet) for salient object detection of strip steel surface defect. Particularly, CKLNet employs a three-stage training strategy. In the first stage, a general SOD model is trained to perform initial detection of surface defect. Then, the second stage constructs a classification branch built upon the general SOD model to extract defect class knowledge. Embarking on this class knowledge, multiple class-specific decoders are trained in the third stage, where we can generate refined and class-aware defect predictions. Extensive experiments on two datasets demonstrate that CKLNet achieves superior detection performance and generalization capability when compared with the cutting-edge lightweight models, with real-time inference speed of 783 FPS on an RTX 2080Ti GPU. Moreover, unlike existing models that only enable defect localization, our CKLNet further provides accurate defect class identification. The source code is publicly available at https://github.com/Kunye-Shen/CKLNet.