YOLOv7 shield tunnel water leakage detection method based on CutMix data augmentation and multi-constraint loss function

doi:10.13474/j.cnki.11-2246.2024.0719

Abstract

Abstract: Since the size of leakage water in the intensity projection image of the shield tunnel is inconsistent and the proportion of pixels is limited, the learning ability of key features of object detection models is weak. As a result, the detection accuracy of leakage disease targets is too low to meet the requirements of the application. Therefore, an improved YOLOv7 shield tunnel leakage water detection method based on CutMix data enhancement and multi-constraint loss function is proposed to address the issue in this paper. Firstly, the tunnel images are enhanced using the embedded CutMix approach. Various training samples are randomly combined into new samples with comprehensive features. Secondly, the YOLOv7 network is employed as the skeletal structure, and an efficient channel attention module is introduced to enhance the ability of crucial leakage features to learn and express themselves autonomously. Finally, a loss function incorporating multi-constraint geometric conditions is designed to improve the accuracy of the geometric shape of the prediction box, thereby improving the model's predictive accuracy. The four algorithms included Fast R-CNN, SSD, YOLOv5, and YOLOv7 are chosen for comparison in complex environments with good lighting, poor lighting, and occlusion. The experiments show that our algorithm achieves a leakage detection accuracy of 85.90%. The average accuracy of the proposed method is higher than Fast R-CNN, SSD, YOLOv5, and YOLOv7 by 5.55%, 8.89%, 3.93%, and 2.75%, respectively. It exhibits good robustness and generalization ability.

Key words: leakage water detection, efficient channel attention, multi-constraint geometric conditions, shield tunneling, YOLOv7

CLC Number:

P258

GAO Xianjun, LIU Zhenyu, XU Lei, HUANG Yifan, TAN Meilin, XIONG Wenhao, YANG Yuanwei. YOLOv7 shield tunnel water leakage detection method based on CutMix data augmentation and multi-constraint loss function[J]. Bulletin of Surveying and Mapping, 2024, 0(7): 105-110.

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