Cloud-Edge Collaborative Continual Adaptation for ITS Object Detection

In the field of Intelligent Transportation Systems (ITS), the challenge of performance degradation in lightweight object detection models on edge devices is significant. This issue primarily arises from environmental changes and shifts in data distribution. The problem is twofold: the limited computational capacity of edge devices, which hinders timely model updates, and the inherent limitations in the generalization capabilities of lightweight models. While large-scale models may have superior generalization, their deployment at the edge is impractical due to computational constraints. To address this challenge, we propose a cloud-edge collaborative continual adaptation learning framework, specifically designed for the DETR model family, aimed at enhancing the generalization ability of lightweight edge models. This framework uses visual prompts to collect and upload data from the edge, which helps to fine-tune cloud-based models for improved target domain generalization. The refined knowledge is then distilled back into the edge models, enabling continuous adaptation to diverse and dynamic conditions. The effectiveness of this approach has been validated through extensive experiments on two datasets for traffic object detection in dynamic environments. The results indicate that our learning method outperforms existing techniques in continual adaptation and cloud-edge collaboration, highlighting its potential in addressing the challenges posed by dynamic environmental changes in ITS.