Priors Meet Hindsight: An Asymmetric Collaborative Evolution Framework for RGB-D Road Defect Detection

Road defect detection represents a fundamental perceptual task for mobile robots and autonomous driving systems; however, existing approaches struggle to balance detection accuracy and computational efficiency on resource-constrained platforms. To overcome this limitation, we propose ACEF, an asymmetric collaborative evolution framework that addresses two fundamental gaps: (i) depth-guided distillation methods tightly couple geometric enhancement with multimodal overhead at inference time, and (ii) temporal knowledge transfer methods rely solely on correlation-based feature alignment across historical states. ACEF establishes a collaborative learning mechanism between a computationally intensive RGB-D teacher network and an efficient RGB student network, incorporating two key innovations: (1) geometry-aware hash injection, which encodes depth features as binary hash attention masks through discrete cosine transform-based frequency-domain decomposition, injecting geometric priors only during training while imposing no overhead at inference time; and (2) counterfactual trajectory retrospection, which grounds recurrent knowledge transfer within a formal structural causal framework. By formalizing causal validity filtering, ACEF retains only those historical states that empirically reduce the geometric learning gap between the teacher and the student, thereby enabling causally selective, rather than purely correlation-based, trajectory guidance. Comprehensive experimental validation demonstrates that ACEF attains an inference speed of 100.8 FPS in a lightweight unimodal setting, outperforming 19 state-of-the-art methods in detection accuracy while maintaining computational practicality for real-world robotic deployment. Additional evaluations across three diverse datasets further verify the generalizability and adaptability of the proposed framework in robotic operating environments. The code is publicly available [here].