HSDNet: Hybrid Super-Resolution Assisted Detection Network for Surface Defect Detection

Le résumé fourni par la source

Surface defect detection plays a vital role in ensuring product quality in real industrial scene. Currently, deep learning-based detectors have achieved notable performance on different open-source high-quality image datasets. Nevertheless, constrained by the deployment costs of imaging equipment in real industrial scenes, the collected images frequently suffer from low resolution (LR). Defect detection on LR images is quite challenging due to the insufficient texture details and blurred boundaries. To address this problem, this paper proposes an advanced two-stage defect detection network, named HSDNet, which consists of an a well-designed Hybrid Super-Resolution (HSR) sub-network and a downstream defect detector RetinaNet. Firstly, the HSR performs effective pixel-level enhancement through the key module, Hybrid Scale Alignment (HSA), comprising the sub-attention modules Expanded Receptive Attention (ERA) and Local Context Attention (LCA), enabling the smooth restoration of multi-scale defect textures and improving both image resolution and quality. Secondly, to preserve potential detection-related features, we introduce an attention selective strategy within the HSR sub-network based on Adaptive Selection Attention (ASA) module. This strategy automatically assigns adaptive parameters to the ERA and LCA modules, thereby improving the representation capability for defect objects of varying scales and sizes. Meanwhile, we adopt a cascaded training strategy, where the downstream frozen detector’s detection results guide the updates of the HSR sub-network, ensuring alignment with detection objectives. Experimental results on two public defect datasets, NEU-DET and PKU-PCB, demonstrate that our proposed HSDNet outperforms several competitive baselines in terms of PSNR, while surpassing other SR methods’ downstream detectors in terms of mAP, proving its effectiveness in defect detection.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.