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

Abstract

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.

Visual Detection of Road Cracks for Autonomous Vehicles Based on Deep Learning

“…This technological progress facilitates automated feature analysis, surpassing traditional, error-prone methods like manual inspection. However, most research [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ] has focused more on crack detection and classification, often neglecting the detailed analysis and measurement of specific crack characteristics [ 41 , 42 , 43 , 44 ]. Study [ 45 ] used laser-scanned range images to classify roadway cracks with a deep convolutional neural network (DCNN).…”

“…Study [ 19 ] developed Panthera, a robotic platform for road crack segmentation and garbage detection. However, their study does not address the limitations of using the mobile mapping system (MMS) for geotagging defects, nor does it explore the technique’s scalability across larger or varied pavement types [ 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ].…”

Image-Enhanced U-Net: Optimizing Defect Detection in Window Frames for Construction Quality Inspection

“…However, challenges remain, including parameter optimization and dataset availability. Some recent research used image enhancement and refinement to improve crack segmentation [10,11]. Our research addresses these challenges, focusing on the domain-specific context of cosmetic quality inspection for window frames.…”

“…The synergistic fusion of DL with a judicious image enhancement (IE) strategy can revolutionize defect identification and reshape the industrial landscape [9]. Recent research, exemplified by Wu et al [10] and Tang et al [11], has effectively utilized these strategies to improve segmentation networks. Wu et al notably enhanced crack segmentation accuracy using MobileNetV2_DeepLabV3, while Tang employed image refinement post-processing with U-Net for similar gains.…”

Brake disc positioning and defect detection method based on improved Canny operator

“…In terms of dimension, to enhance detection accuracy, Wu et al. [12] proposed a lightweight MobileNetV2_DeepLabV3 image segmentation network. The method of inscribing circles within crack contours was utilized to calculate the maximum width of cracks, resulting in a reduced error rate of 11.22%.…”