Decoupled Topology Distance Distillation for Lightweight Smoke Detection in Aerial Remote Sensing Images

Early aerial smoke detection is vital for wildfire response, but deploying accurate two-stage deep detectors on resource-limited Unmanned Aerial Vehicles (UAVs) remains computationally prohibitive. Moreover, under uniform supervision, standard knowledge distillation struggles on aerial smoke data, where foreground–background imbalance is severe and smoke boundaries are visually ambiguous. To resolve this, we propose the Decoupled Topology Distance Distillation (DeTD) framework to compress two-stage smoke detectors for real-time edge inference. DeTD features three key innovations. First, a decoupling module uses ground-truth-derived binary masks to isolate smoke and background features, mitigating distillation class imbalance. Second, a topology distance distillation module projects these decoupled features onto a unit hypersphere, employing a novel Symmetric Triplet Loss. This jointly optimizes the intra-class compactness and inter-class separability of both the foreground and background relational geometry between the teacher and student networks. Third, prediction-head soft-label distillation transfers class-conditional knowledge, synergistically complementing the intermediate-feature distillation. Comprehensive experiments on the D-Fire benchmark and a custom aerial UAV dataset yield mAP50 scores of 67.4% and 70.6%, respectively. DeTD consistently outperforms thirteen recent distillation baselines, and the lightweight student attains real-time-compatible inference, narrowing the accuracy–efficiency gap and indicating feasibility for deployment on resource-constrained UAV edge hardware.