DMDSNet: A Computer Vision-based Dual Multi-task Model for Tunnel Bolt Detection and Corrosion Segmentation

Tan, Lei; Chen, Xiaohan; Hu, Xiaoxi; Tang, Tao

doi:10.1109/itsc57777.2023.10421958

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) 2023

DOI: 10.1109/itsc57777.2023.10421958

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DMDSNet: A Computer Vision-based Dual Multi-task Model for Tunnel Bolt Detection and Corrosion Segmentation

Lei Tan,

Xiaohan Chen,

Xiaoxi Hu

et al.

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2024

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Cited by 2 publications

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Spatial Adaptive Improvement Detection Network for Corroded Bolt Detection in Tunnels

Guo,

Cheng,

Xie

et al. 2024

Buildings

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The detection of corroded bolts is crucial for tunnel safety. However, the specific directionality and complex texture of corroded bolt defects make current YOLO series models unable to identify them accurately. This study proposes a spatial adaptive improved detection network (SAIDN), which integrates a spatial adaptive improvement module (SAIM) that adaptively emphasizes important features and reduces interference, enhancing detection accuracy. The SAIM performs a detailed analysis and transformation of features in the spatial and channel dimensions, enhancing the model’s ability to recognize critical defect information. The use of depthwise separable convolutions and adaptive feature reweighting strategies improves detail processing capabilities and computational efficiency. Experimental results show that SAIDN significantly outperforms existing models in detection accuracy, achieving 94.4% accuracy and 98.5% recall, surpassing advanced models such as YOLOv9 and Cascade RCNN. These findings highlight the potential of SAIDN in enhancing subway tunnels’ safety and maintenance efficiency.

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Spatial Adaptive Improvement Detection Network for Corroded Bolt Detection in Tunnels

Guo,

Cheng,

Xie

et al. 2024

Buildings

View full text Add to dashboard Cite

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DAEiS-Net: Deep Aggregation Network with Edge Information Supplement for Tunnel Water Stain Segmentation

Wang,

Huang,

Zheng

et al. 2024

Sensors

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Tunnel disease detection and maintenance are critical tasks in urban engineering, and are essential for the safety and stability of urban transportation systems. Water stain detection presents unique challenges due to its variable morphology and scale, which leads to insufficient multiscale contextual information extraction and boundary information loss in complex environments. To address these challenges, this paper proposes a method called Deep Aggregation Network with Edge Information Supplement (DAEiS-Net) for detecting tunnel water stains. The proposed method employs a classic encoder–decoder architecture. Specifically, in the encoder part, a Deep Aggregation Module (DAM) is introduced to enhance feature representation capabilities. Additionally, a Multiscale Cross-Attention Module (MCAM) is proposed to suppress noise in the shallow features and enhance the texture information of the high-level features. Moreover, an Edge Information Supplement Module (EISM) is designed to mitigate semantic gaps across different stages of feature extraction, improving the extraction of water stain edge information. Furthermore, a Sub-Pixel Module (SPM) is proposed to fuse features at various scales, enhancing edge feature representation. Finally, we introduce the Tunnel Water Stain Dataset (TWS), specifically designed for tunnel water stain segmentation. Experimental results on the TWS dataset demonstrate that DAEiS-Net achieves state-of-the-art performance in tunnel water stain segmentation.

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DMDSNet: A Computer Vision-based Dual Multi-task Model for Tunnel Bolt Detection and Corrosion Segmentation

Cited by 2 publications

References 14 publications

Spatial Adaptive Improvement Detection Network for Corroded Bolt Detection in Tunnels

Spatial Adaptive Improvement Detection Network for Corroded Bolt Detection in Tunnels

DAEiS-Net: Deep Aggregation Network with Edge Information Supplement for Tunnel Water Stain Segmentation

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