A Novel Hybrid Approach for Concrete Crack Segmentation Based on Deformable Oriented-YOLOv4 and Image Processing Techniques

Abstract

Regular crack inspection plays a significant role in the maintenance of concrete structures. However, most deep-learning-based methods suffer from the heavy workload of pixel-level labeling and the poor performance of crack segmentation with the presence of background interferences. To address these problems, the Deformable Oriented YOLOv4 (DO-YOLOv4) is first developed for crack detection based on the traditional YOLOv4, in which crack features can be effectively extracted by deformable convolutional layers, and the crack regions can be tightly enclosed by a series of oriented bounding boxes. Then, the proposed DO-YOLOv4 is further utilized in combination with the image processing techniques (IPTs), leading to a novel hybrid approach, termed DO-YOLOv4-IPTs, for crack segmentation. The experimental results show that, owing to the high precision of DO-YOLOv4 for crack detection under background noise, the present hybrid approach DO-YOLOv4-IPTs outperforms the widely used Convolutional Neural Network (CNN)-based crack segmentation methods with less labeling work and superior segmentation accuracy.