Weakly Supervised Patch Label Inference Network With Image Pyramid For Pavement Diseases Recognition In The Wild

Automatic pavement disease recognition is vital for pavement maintenance and management. In this paper, we present an end-to-end deep learning approach named Weakly Supervised Patch Label Inference Network with Image Pyramid (WSPLIN-IP) for recognizing various types of pavement diseases that are not just limited to the specific ones, such as crack and pothole. WSPLIN-IP first divides the pavement image into patches with an image pyramid for fully exploiting the resolution and scale information. Then, a Patch Label Inference Network (PLIN) is employed for inferring the labels of these patches constrained with a patch label sparsity loss. Finally, the patch labels are fed into a Comprehensive Decision Network (CDN) for disease recognition. Since only the image label is available during whole training, the training of PLIN is conducted in a weakly supervised way under the guidance of CDN. Moreover, the trained PLIN can provide the interpretable intermediate information. We evaluate our method on a large-scale Bituminous Pavement Disease Dataset named CQU-BPDD whose samples are acquired in the wild. Extensive results demonstrate the superiority of our method over baselines.