To resolve the issue of degraded detection accuracy for unmanned aerial vehicle object detection under low-illumination environments, this paper introduces a parallel object detection model. First, a dual-branch architecture is established by parallelly integrating a Zero-Reference Deep Curve Estimation (Zero-DCE) illumination enhancement network with a You Only Look Once (YOLOv11n)-based object detection network, enabling collaborative feature training and real-time updates. Through a feature-sharing mechanism, the two branches are jointly optimized during training, thus enhancing the model’s generalization capability in low-illumination environments. Furthermore, to further improve detection accuracy, a Dynamic Pooling Synergy Attention (DPSA) module is introduced into the backbone of YOLOv11n. By integrating dynamic pooling-based channel attention with spatial attention, this module improves feature representation, improves performance under complex environments, and increases adaptability to multi-scale targets. In addition, a High and Low Frequency Spatially-adaptive Feature Modulation (HLSAFM) module is added to the detection network’s Neck. Through high- and low-frequency feature refinement, segmented feature processing, and dynamic modulation, the network is able to capture richer feature information, thereby strengthening feature representation and discriminative capability. Extensive experiments on the VisDrone (Night) and DroneVehicle (Night) datasets demonstrate superior performance over multiple existing methods under low-illumination object detection tasks. Compared with the original YOLOv11n model, the proposed model mAP50 increases by 6.0% and 1.0% and mAP50:95 increases by 3.1% and 0.8%, respectively. These results confirm the enhanced detection capability achieved by our method in challenging low-illumination unmanned aerial vehicle (UAV) scenarios.
Liu et al. (Sat,) studied this question.