A Lightweight Small-UAV Detection via Synergistically Enhanced YOLOv11
Source abstract
Abstract
Detecting small unauthorized UAVs against complex backgrounds is challenging: targets can be just a few pixels wide, background clutter is pervasive, and the detection system must run on resource-constrained edge hardware. This paper presents a lightweight detector built on YOLOv11 that jointly addresses background suppression and edge-deployment efficiency. A serial channel-spatial attention module is embedded into the backbone to sharpen target features and suppress interference. DySample preserves edge details during resolution recovery, and the detection head is restructured by adding a high-resolution P2 branch while removing the large-object P5 head, shifting the focus to small targets without inflating the model. The WIoU v3 loss function stabilizes training by automatically down-weighting low-quality samples. On the DUT Anti-UAV dataset, the proposed model obtains 92.2% mAP@0.5 and 61.5% mAP@0.5:0.95 with only 2.11 M parameters, a reduction of 18.2% relative to the YOLOv11n baseline. Its ability to generalize across different scenarios is verified by cross-dataset evaluation on Det-Fly and LRDDv2. Deployed on an NVIDIA Jetson Orin Nano Super with TensorRT FP16 optimization, the system achieves 37 end-to-end FPS. These findings demonstrate that the presented method significantly improves small-target detection in complex backgrounds while maintaining a lightweight design suitable for real-time edge deployment.
Source: crossref · Quality: baseline
Affiliations
School of Teachers College for Vocational and Technical Education, Guangxi Normal University, Guilin 541004, China
Guangxi University Engineering Research Center of Agricultural and Forestry Intelligent Equipment Technology, Guilin 546300, China
School of Information and Communication Engineering, North University of China, Taiyuan 030051, China
Funding
Science and Technology Planning Project of Guangxi Province · Guangxi Normal University Autonomous Region-Level Undergraduate Innovation Training Program Project · National Natural Science Foundation of China · Science and Technology Cooperation Project of Shanxi Province · Industry–University–Research Innovation Fund Projects of China University · Fund Project of the Key Laboratory of AI and Information Processing
References (37)
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