An Enhanced Percolation Method for Automatic Detection of Cracks in Concrete Bridges

Abstract

As cracks on concrete bridges become severer and more frequent, methods of detecting cracks on concrete bridges have aroused great concern. Conventional methods, e.g., manual detection and equipment-aided detection, suffer from subjectivity and inefficiency, which increases demands for an accurate and efficient method to detect bridge cracks. To this end, we modify the existing percolation method and propose an enhanced percolation method, which detects cracks of concrete bridges automatically. The modification includes three improvements, which are (1) employing photo expansion to eliminate boundary effects, (2) varying shape factors to increase the accuracy of percolating unclear cracks, and (3) decreasing the number of neighbouring pixels to form candidate sets. Combined with the above three improvements, three versions of enhanced percolation methods utilizing three different shape factors are put forward. The numerical experiment on detecting cracks in 200 images of the bridge surface demonstrates the outperformance of the enhanced percolation method in precision, recall, F-1 score, and time compared with traditional detecting methods. The proposed method can be generalized on the application of detecting other types of bridge diseases, which is an advantage for designing, maintaining, and restoring infrastructures.