Enhanced Precision in Dam Crack Width Measurement: Leveraging Advanced Lightweight Network Identification for Pixel-Level Accuracy

分割 计算机科学 人工智能 可靠性(半导体) 深度学习 影子(心理学) 计算机视觉 像素 模式识别(心理学) 心理学 功率(物理) 物理 量子力学 心理治疗师
作者
Zihao Wu,Yunchao Tang,Bo Hong,Bojian Liang,Yuping Liu
出处
期刊:International Journal of Intelligent Systems [Wiley]
卷期号:2023: 1-16 被引量:30
标识
DOI:10.1155/2023/9940881
摘要

In dam engineering, the presence of cracks and crack width are important indicators for diagnosing the health of dams. The accurate measurement of cracks facilitates the safe use of dams. The manual detection of such defects is unsatisfactory in terms of cost, safety, accuracy, and the reliability of evaluation. The introduction of deep learning for crack detection can overcome these issues. However, the current deep learning algorithms possess a large volume of model parameters, high hardware requirements, and difficulty toward embedding in mobile devices such as drones. Therefore, we propose a lightweight MobileNetV2_DeepLabV3 image segmentation network. Furthermore, to prevent interference by noise, light, shadow, and other factors for long-length targets when segmenting, the atrous spatial pyramid pooling (ASPP) module parameters in the DeepLabV3+ network structure were modified, and a multifeature fusion structure was used instead of the parallel structure in ASPP, allowing the network to obtain richer crack features. We collected the images of dam cracks from different environments, established segmentation datasets, and obtained segmentation models through network training. Experiments show that the improved MobileNetV2_DeepLabV3 algorithm exhibited a higher crack segmentation accuracy than the original MobileNetV2_DeepLabV3 algorithm; the average intersection rate attained 83.23%; and the crack detail segmentation was highly accurate. Compared with other semantic segmentation networks, its training time was at least doubled, and the total parameters were reduced by more than 2 to 7 times. After extracting cracks through the semantic segmentation, we proposed to use the method of inscribed circle of crack outline to calculate the maximum width of the detected crack image and to convert it into the actual width of the crack. The maximum relative error rate was 11.22%. The results demonstrated the potential of innovative deep learning methods for dam crack detection.
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