Prof. Chuan Hui Wu Profile

Prof. Chuan Hui Wu

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Proceedings Article | 13 June 2024 Paper

Recognition method of subway tunnel leakage diseases based on semantic segmentation

Qingsong Zhao, Chuanhui Wu, Wenhao Wu

Proceedings Volume 13180, 131805K (2024) https://doi.org/10.1117/12.3034117

KEYWORDS: Image segmentation, Diseases and disorders, Semantics, Feature extraction, Image processing

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In order to accurately segment water leakage defects in subway tunnel lining images, a semantic segmentation improvement algorithm based on DeepLabV3+ was proposed. First, the lightweight network MobileNetV2 was used as the backbone network to effectively reduce the number of parameters; Second, the ASPP module was improved by connecting the atrous convolutions with different dilation rates to obtain rich contextual information; Then, the CBAM module was added to the coding layer structure to amplify the weight of the effective feature layer and improve the model's ability to perceive the water leakage region. The ResNet module was added to the decoding layer to fuse shallow and deep features to enrich the detail information and edge information. Finally, Focal Loss was used as the loss function to solve the problem of imbalance in the proportion of category pixels. The experimental results show that the F1, score and MIoU of the model on the test set reach 87.59% and 81.24%, and the improved model is able to recognize and segment the leakage disease effectively.

Proceedings Article | 20 October 2023 Paper

Subway tunnel crack detection method based on image processing

Chun Shuang Fu, Chuan Hui Wu, Qing Song Zhao

Proceedings Volume 12916, 1291602 (2023) https://doi.org/10.1117/12.3005008

KEYWORDS: Image filtering, Digital filtering, Image segmentation, Image processing, Gaussian filters, Image processing algorithms and systems, Binary data

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The crack of subway tunnel seriously affects the service life of the tunnel and endangers the safety of subway traffic. The efficiency of manual detection is low, this paper proposes a tunnel crack detection method based on image processing. The images of tunnel cracks collected inside the subway tunnel suffer from problems such as uneven lighting, low contrast, and severe noise, which affect the recognition of cracks. To address these issues, a multi-scale Retinex algorithm combined with linear stretching is firstly used to preprocess the images, effectively balancing the lighting. Secondly, an improved adaptive median filter algorithm is used to filter out image noise while effectively preserving the crack edges. Thirdly, the Scharr operator combined with the Otsu method is used to segment the filtered image, effectively separating the crack area. Fourth, the crack binary image without noise is obtained using connected domain filtering and morphological processing. Finally, the crack length, average width, maximum width, and area are calculated using the crack skeleton image. The research results show that the proposed algorithm can effectively identify crack areas, demonstrating its effectiveness.

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