Joint Training of Image Generator and Detector for Road Defect Detection
This work addresses road defect detection for road authorities, enabling more efficient deployment on edge devices, though it is incremental as it builds on existing generative and detection methods.
The authors tackled road defect detection by jointly training an image generator and detector to synthesize high-quality defect images for data augmentation, achieving state-of-the-art performance on the RDD2022 benchmark without using ensemble methods or test-time augmentation, with less than 20% of the parameters compared to baselines.
Road defect detection is important for road authorities to reduce the vehicle damage caused by road defects. Considering the practical scenarios where the defect detectors are typically deployed on edge devices with limited memory and computational resource, we aim at performing road defect detection without using ensemble-based methods or test-time augmentation (TTA). To this end, we propose to Jointly Train the image Generator and Detector for road defect detection (dubbed as JTGD). We design the dual discriminators for the generative model to enforce both the synthesized defect patches and overall images to look plausible. The synthesized image quality is improved by our proposed CLIP-based Fréchet Inception Distance loss. The generative model in JTGD is trained jointly with the detector to encourage the generative model to synthesize harder examples for the detector. Since harder synthesized images of better quality caused by the aforesaid design are used in the data augmentation, JTGD outperforms the state-of-the-art method in the RDD2022 road defect detection benchmark across various countries under the condition of no ensemble and TTA. JTGD only uses less than 20% of the number of parameters compared with the competing baseline, which makes it more suitable for deployment on edge devices in practice.