Classifying cracks at sub-class level in closed circuit television sewer inspection videos - Zuo, Xin; Dai, Bin; Shan, Yongwei; Shen, Jifeng; Hu, Chunlong; Huang, Shucheng (Automation in construction, 2020)
This paper presents a novel computer vision based system to support automated PACP (Pipeline Assessment Certification Program) coding for cracks. The proposed system comprises five major steps: 1) identifying forward facing view (FFV) with pipeline viewpoint detector, 2) obtaining stable edge information using structure edge detector, 3) acquiring crack segments and inner circle area of the pipeline with image binarization, 4) generating a 2D angular-diameter histogram for each frame, and 5) training a crack category classifier with support vector machine (SVM). The experimental results demonstrated that the proposed system can not only detect the cracks and categorize the crack type per PACP standard effectively but can also run about 10 frames per second (fps) in real world CCTV videos with 320 × 240 resolutions. In terms of accuracy in detecting cracks, the proposed method reaches about 91%, 88% and 90% recall for longitudinal cracks (CL), circumferential cracks (CC) and multiple cracks (CM), respectively. This paper contributes to the overall body of knowledge by providing an innovative framework that supports real-time crack identification and coding per PACP standards, which will lay a strong foundation for the development of a fully automated PACP sewer pipeline inspection system. •Algorithms to classify subclass level cracks in sewer inspections were proposed.•The recall rate of the proposed crack classification algorithm can reach over 90%.•The algorithm showed sound performance while video running at a speed of 10 pfs.