| Paper ID | ASPS-5.2 |
| Paper Title |
Weakly Supervised Patch Label Inference Network with Image Pyramid for Pavement Diseases Recognition in the Wild |
| Authors |
Guixin Huang, Sheng Huang, Chongqing University, China; Luwen Huangfu, San Diego State University, United States; Dan Yang, Chongqing University, China |
| Session | ASPS-5: Audio & Images |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 16:30 - 17:15 |
| Presentation Time: | Thursday, 10 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Applied Signal Processing Systems: Signal Processing Systems [DIS-EMSA] |
| IEEE Xplore Open Preview |
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| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
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. |
