Manhole cover defect recognition is of significant practical importance as it can accurately identify damaged or missing covers, enabling timely replacement and maintenance. Traditional manhole cover detection techniq...Manhole cover defect recognition is of significant practical importance as it can accurately identify damaged or missing covers, enabling timely replacement and maintenance. Traditional manhole cover detection techniques primarily focus on detecting the presence of covers rather than classifying the types of defects. However, manhole cover defects exhibit small inter-class feature differences and large intra-class feature variations, which makes their recognition challenging. To improve the classification of manhole cover defect types, we propose a Progressive Dual-Branch Feature Fusion Network (PDBFFN). The baseline backbone network adopts a multi-stage hierarchical architecture design using Res-Net50 as the visual feature extractor, from which both local and global information is obtained. Additionally, a Feature Enhancement Module (FEM) and a Fusion Module (FM) are introduced to enhance the network’s ability to learn critical features. Experimental results demonstrate that our model achieves a classification accuracy of 82.6% on a manhole cover defect dataset, outperforming several state-of-the-art fine-grained image classification models.展开更多
文摘Manhole cover defect recognition is of significant practical importance as it can accurately identify damaged or missing covers, enabling timely replacement and maintenance. Traditional manhole cover detection techniques primarily focus on detecting the presence of covers rather than classifying the types of defects. However, manhole cover defects exhibit small inter-class feature differences and large intra-class feature variations, which makes their recognition challenging. To improve the classification of manhole cover defect types, we propose a Progressive Dual-Branch Feature Fusion Network (PDBFFN). The baseline backbone network adopts a multi-stage hierarchical architecture design using Res-Net50 as the visual feature extractor, from which both local and global information is obtained. Additionally, a Feature Enhancement Module (FEM) and a Fusion Module (FM) are introduced to enhance the network’s ability to learn critical features. Experimental results demonstrate that our model achieves a classification accuracy of 82.6% on a manhole cover defect dataset, outperforming several state-of-the-art fine-grained image classification models.
