Dam surface crack detection method based on improved DeepLabV3+ network
ZHOU Xun1,2,3, LI Yonglong3, ZHOU Yingyue1,2, WANG Haoran3, LI Jiayang1,2, ZHAO Jiaqi1,2
1. School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China; 2. Sichuan Provincial Key Laboratory Robot Technology Used for Special Environment, Southwest University of Science and Technology, Mianyang 621000, China; 3. Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610213, China
Abstract：Image analysis is an efficient and accurate method for identifying hydropower dam surface defects. However, due to the complex background of dam crack images and the uneven proportion of cracks and background pixels, the detection effect of traditional algorithms is poor. Moreover, traditional artificial crack inspection is not only inefficient but also costly in the present day. Efficient and accurate dam surface crack detection techniques are crucial for dam maintenance and operation. In order to achieve accurate and efficient dam surface crack detection, a dam surface crack detection method based on an improved DeepLabV3+ model is proposed. Model training is carried out for the self-made dam surface crack image dataset of a hydropower station in Southwest China, and the model is evaluated by F1,score, ZMIoU, ZMPA, parameter quantity and other indicators. The following improvements are made to the traditional DeepLabV3+ network model: (1) A three line attention module is added to improve the model's ability to extract crack pixels and reduce proportion imbalance between background pixels and crack pixels. (2) The original pyramid pooling module is cascaded for model optimization so that the model can achieve more intensive pixel sampling, and subsequently obtain more abundant crack features. (3) In order to solve the problem of the significant/too large number of traditional DeepLabV3+ network parameters, MobileNetV2 network is used as the backbone of the model to extract the network, to reduce the network to a lightweight module, and to reduce model parameters. (4) Focal loss and Dice loss are used as the loss functions of the model to overcome the data imbalance and to improve the accuracy of network classification. The improved DeepLabV3+ network model in this paper could better realize the extraction of crack pixels, reduce the problems caused by the imbalance of pixel proportion, and better ensure the efficient and accurate detection of dam surface cracks. The experiment on the self-made dam surface crack image dataset of a hydropower station in Southwest China showed the following: (1) Compared with the original model, the improved DeepLabV3+ model increased F1, score by 3.33%, ZMIOU by 2.89%, ZMPA by 1.12%, and the parameters were reduced to 3 014 714. This finding showed that the improved model proposed in this paper had stronger performance than the original model, better ability to extract crack pixels, and could better complete the task of crack identification. (2) Compared with other attention mechanisms, the three line attention module proposed in this paper had certain advantages, which could increase the attention of the model to the crack pixels and enable the model to extract the crack features needed. Through an analysis of the experimental results, the model improved in this paper has stronger segmentation ability, less missing data and false detection, and can effectively complete the dam surface crack segmentation task. The improved method increases the efficiency and accuracy of dam surface crack detection and reduces the model parameters. It can provide powerful data support for crack detection and the safe operation of hydropower projects.
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2023, Vol. 63 
