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 Super-vised 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 and 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 real world. Extensive results demonstrate the superiority of our method over baselines.