A Pavement Disease Detection Method based on the Improved Mask R-CNN

Abstract

Accurate detection and location of pavement disease has attracted more and more research interest. Existing approaches depend heavily on manual works and expert experience, which are time-consuming and cost-effective. The paper presents an automated pavement damage detection and location method by adopting Mask R-CNN algorithm and active contour model. Mask R-CNN algorithm selects ResNet as the backbone network because of its strong feature extraction ability, and generates candidate target regions through 12 different anchors to further improve the accuracy of detection. Then, it is sent to the sub-network for classification and positioning. And contour of the diseased region is generated by Mask R-CNN. The region of the disease can be a preparatory knowledge for later road maintenance. But Mask R-CNN leads to the missing area due to inherent defects, the paper adopts active contour model to address the problem. The initial curve of the active contour model comes from the result of Mask R-CNN model and evolves under the action of internal energy and external energy. When the energy functional reaches the minimum, the curve converges to the edge of the object. The experimental result for pavement disease image shows the proposed method has desirable performance.