Detection of urban underground sewage pipeline system based on YOLOv5-Shufflenet lightweight model

Haibo Zhou; Zekuan Zhao; Xiaofeng Liu

doi:10.1117/12.2680902

23 May 2023 Detection of urban underground sewage pipeline system based on YOLOv5-Shufflenet lightweight model

Haibo Zhou, Zekuan Zhao, Xiaofeng Liu

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 126450E (2023) https://doi.org/10.1117/12.2680902
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

Most existing urban underground sewage pipeline systems (hereinafter referred to as sewer systems) determine the existence and type of faults by manually checking videos or even entering the pipeline directly, which is a time-consuming and laborious process. Some automatic image searching based on traditional determination rules have low generalization and accuracy. Although the target recognition detection model based on the depth learning method requires a certain number of labeled images for learning and training, it improves the accuracy and strengthens the generalization. In this paper, we collected and labeled 2330 pictures as the data set and improved a deep learning model for sewer fault detection. The experiment proved that the detection accuracy of this technology for cracks, pipe deformation and dislocation, silting, corrosion, and falling off faults reached 80.5%, 91%, 94.1%, 87.5%, 88.9% respectively. The mAP value of target detection reached 88.4%.

Citation Download Citation

Haibo Zhou, Zekuan Zhao, and Xiaofeng Liu "Detection of urban underground sewage pipeline system based on YOLOv5-Shufflenet lightweight model", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 126450E (23 May 2023); https://doi.org/10.1117/12.2680902

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