Multi-Scale attention and hierarchical feature extraction-based network for metal surface defect detection
Year: 2025
Type: article
Abstract: Surface defects in metal components hold significant implications for product quality and reliability, yet conventional methods suffer from the challenge of efficient and accurate detection. To address this, a novel detection network (YOLO-MAH) based on multi-scale attention and hierarchical feature extraction is proposed in this paper. First, a tiered multi-scale attention (TMA) module is constructed to enhance detail perception through the information operation method of multi-level feature progressive stacking. Second, a hierarchical feature extraction (HFE) module is developed, employing multi-scale convolutions and shortcut to establish a parallel multi-branch structure for cross-scale feature extraction while minimizing original information loss. Finally, an expanse-aware spatial pyramid pooling (EA-SPP) module is designed, leveraging multi-stage convolutional integration to expand the receptive field and facilitate multi-scale feature fusion. Experimental results on three public datasets and one self-built dataset demonstrate that YOLO-MAH achieves superior performance, with mean average precision (mAP50) improvements of 1.3%-7.7% over baseline models. Further comparisons with state-of-the-art models validate the effectiveness and generalization capability of YOLO-MAH, exhibiting its strong potential for industrial inspection applications.
Source: Egyptian Informatics Journal
Authors: Siwen. Xu, Hanning. Chen, Rui. Ni, Maowei. He, Zhaodi. Ge, Xiaodan. Liang
Institution: Tiangong University
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,200
OpenAlex ID: W4416521543