通过联合训练生成器与检测器，提升道路缺陷检测精度并降低部署开销。
Joint Training of Image Generator and Detector for Road Defect Detection
- 生成器与检测器协同训练，生成更具挑战性的合成缺陷图像用于数据增强。
- 在不使用集成或测试时增强的情况下，性能超越现有最优方法，参数量不足1/5。
- 适合资源受限的边缘设备部署，尤其适用于实际道路巡检场景。
道路缺陷检测对减少车辆损伤至关重要。针对检测器常部署于内存与算力有限的边缘设备这一现实场景，本文提出一种无需集成方法或测试时增强（TTA）的道路缺陷检测联合训练框架（JTGD）。JTGD设计双判别器以确保合成缺陷区域及整体图像的真实性；通过基于CLIP的弗雷歇初始距离损失提升合成图像质量。生成模型与检测器联合训练，促使生成器合成更难样本以强化检测器。由于采用了高质量且更具挑战性的合成图像进行数据增强，JTGD在无集成与TTA条件下，于跨国家的RDD2022基准上表现优于现有最先进方法。其参数量不足基线模型的20%，更适用于边缘设备实际部署。
原文摘要 · Abstract (English)
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.
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