Analysis of Edge-Detection Techniques for Crack Identification in Bridges

TLDR

Bridge monitoring and maintenance is essential yet costly, and traditional visual inspections require inspectors to travel to each bridge to assess deterioration, with crack detection being a key component. The study aims to automate crack detection to reduce costs and enable more frequent inspections by comparing the effectiveness of four edge‑detection techniques. The authors implemented four edge‑detection algorithms—fast Haar transform, fast Fourier transform, Sobel, and Canny—in MATLAB and evaluated them on 50 concrete bridge images, half with cracks and half without. The fast Haar transform proved significantly more reliable than fast Fourier transform, Sobel, and Canny for crack identification.

Abstract

Bridge monitoring and maintenance is an expensive yet essential task in maintaining a safe national transportation infrastructure. Traditional monitoring methods use visual inspection of bridges on a regular basis and often require inspectors to travel to the bridge of concern and determine the deterioration level of the bridge. Automation of this process may result in great monetary savings and can lead to more frequent inspection cycles. One aspect of this automation is the detection of cracks and deterioration of a bridge. This paper provides a comparison of the effectiveness of four crack-detection techniques: fast Haar transform (FHT), fast Fourier transform, Sobel, and Canny. These imaging edge-detection algorithms were implemented in MatLab and simulated using a sample of 50 concrete bridge images (25 with cracks and 25 without). The results show that the FHT was significantly more reliable than the other three edge-detection techniques in identifying cracks.