Design and Implementation of Bridge Structural Crack Detection System Based on Convolutional Neural Network (CNN)

Bridges are crucial for public safety and economic activities; thus, their structural integrity must be the priority concern; however, the current crack inspection methods are slow, depend on personal judgment, and do not 100% eliminate human error. The fading of old infrastructures, accompanied by the increase in traffic, has led to the need for an immediate, accurate, and efficient automatic crack detection system. The current aim of the project is the realization of a Bridge Structural Crack Detection System that is based on a Golden Tortoise Beetle Weighted Convolutional Neural Network (GTB-WCNN) to achieve credible and fast crack detection. In order to simulate a wide range of scenarios regarding light, material, and surface, high-quality images of the bridge surfaces were taken and supplemented by UAV-based field image capturing. In the pre-processing phase, Contrast-Limited Adaptive Histogram Equalization (CLAHE) was employed to enhance contrast and make the cracks more visible while simultaneously Gaussian filtering was used to minimize noise. The use of Histogram of Oriented Gradients (HOG) for feature extraction not only made it possible to collect the necessary edge and gradient features but also resulted in the very fine distinction between microcracks and background textures, thus leading to a better performance of crack identification in difficult-to-interpret photos of bridges. The GTB-WCNN model surpassed traditional ones by a significant margin in terms of the above-mentioned metrics, achieving 98.33% of detection accuracy, 97.61% of specificity, 98.21% of F1-score, 98.41% of precision, and 98.21% of recall. The model also expresses its strong generalization ability by being able to detect both small and large-scale cracks in different situations.