Multimodal object detection is currently a research hotspot in computer vision. However, the fusion of visible and infrared modalities inevitably increases computational complexity, making most high-performance detection models difficult to deploy on resource-constrained UAV edge devices. Although pruning and knowledge distillation are widely used for model compression, applying them independently often leads to an unstable accuracy–efficiency trade-off. Therefore, this paper proposes a hybrid lightweight algorithm named SAMKD, which combines selective activation pruning with masked knowledge distillation in a staged manner to improve efficiency while maintaining detection performance. Specifically, the selective activation network pruning model (SAPM) first reduces redundant computation by dynamically adjusting network weights and the activation state of input data to generate a lightweight student network. Then, the mask binary classification knowledge distillation (MBKD) strategy is introduced to compensate for this degradation by guiding the student network to recover missing representation patterns under masked feature learning. Moreover, MBKD reformulates classification logits into multiple foreground–background binary mappings, effectively alleviating the severe foreground–background imbalance commonly observed in UAV aerial imagery. This paper constructs a multimodal UAV aerial imagery object detection dataset, M2UD-18K, which includes 9 types of targets and over 18,000 pairs. Extensive experiments show that SAMKD performs well on the self-constructed M2UD-18K dataset, as well as the public DroneVehicle dataset, achieving a favorable trade-off between detection accuracy and detection speed.
Lu et al. (Sun,) studied this question.