Learning to Detect Objects Under Inclement Weather Conditions via Symmetric Localization Distillation and Adaptive Label Assignment

Robust object detection under varying weather conditions (e.g., rain, fog, and snow) presents significant challenges for industrial vision systems due to inherent visual degradations in manufacturing sites and outdoor facilities. While knowledge distillation offers promising potential by feature imitating and logit mimicking, existing methods face two critical limitations: first, inadequate mechanisms for effectively transferring localization capabilities in the presence of severe image degradation, and second, suboptimal strategies for identifying optimal distillation regions. To address these issues, we present a symmetric localization distillation loss based on the Jensen–Shannon divergence. Its mathematical characteristics, e.g., boundedness, symmetry, and gradient smoothness, enable robust preservation of spatial relationships and stabilize training processes. In addition, we present an adaptive label assignment strategy to select distillation regions, thus reducing the sparsity of positive samples during our knowledge distillation process. This work is the first one to apply knowledge distillation to object detection in inclement weather conditions. Extensive experiments on three challenging datasets show that our method improves the student model's object detection accuracy while maintaining its inference speed.