Abstract
Cracks are one of the most common factors that affect the quality of concrete surfaces, so it is necessary to detect concrete surface cracks. However, the current method of manual crack detection is labor-intensive and time-consuming. This study implements a novel lightweight neural network based on the YOLOv4 algorithm to detect cracks on a concrete surface in fog. Using the computer vision algorithm and the GhostNet Module concept for reference, the backbone network architecture of YOLOv4 is improved. The feature redundancy between networks is reduced and the entire network is compressed. The multi-scale fusion method is adopted to effectively detect cracks on concrete surfaces. In addition, the detection of concrete surface cracks is seriously affected by the frequent occurrence of fog. In view of a series of degradation phenomena in image acquisition in fog and the low accuracy of crack detection, the network model is integrated with the dark channel prior concept and the Inception module. The image crack features are extracted at multiple scales, and BReLU bilateral constraints are adopted to maintain local linearity. The improved model for crack detection in fog achieved an mAP of 96.50% with 132 M and 2.24 GMacs. The experimental results show that the detection performance of the proposed model has been improved in both subjective vision and objective evaluation metrics. This performs better in terms of detecting concrete surface cracks in fog.
Highlights
Controlling concrete surface quality is one of the main challenges facing the concrete industry
To verify the concrete surface crack detection performance of the lightweight YOLOv4 model proposed in this paper, the experimental results are compared with those of the original YOLOv4 model
A crack detection method based on the YOLOv4 algorithm is proposed, which provides a more accurate, efficient and intelligent method for the detection of cracks on concrete surfaces
Summary
Controlling concrete surface quality is one of the main challenges facing the concrete industry. High quality concrete surfaces leave an aesthetically pleasing impression, so architects and building owners are getting stricter about concrete surface quality (Chen et al, 2019; Wei et al, 2019). One of the most common affecting factors for concrete surface quality, has a significance impact on the safety and sustainability of concrete buildings. Crack detection plays an essential role in maintaining buildings. Human visual inspection was often used to assess defects on concrete surfaces (Peng et al, 2020). The judgment conclusions drawn by different people are diverse under the identical concrete surface conditions (Laofor and Peansupap, 2012). Automatic defect inspection is extremely feasible to assess defects more efficiently and objectively
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