LSO-YOLO: a Lightweight Real-Time Object Detection Network for UAV Small-Object Detection

Driven by the rapid expansion of the low-altitude economy, unmanned aerial vehicles (UAVs) have emerged as pivotal platforms for aerial perception. However, balancing detection accuracy and computational efficiency for micro-targets remains a formidable challenge. To address this, we propose LSO-YOLO, a novel real-time detector optimized for UAV-based small-object detection. Our primary contributions are threefold: first, a multi-scale dilated context aggregation (MDCA) module is designed to replace the final down-sampling convolution in the backbone. By employing sequential dilated depth-wise convolutions and a lightweight channel attention mechanism, the MDCA expands the receptive field and suppresses background noise, effectively mitigating the loss of fine-grained details caused by aggressive down-sampling; second, the C2f module in the neck is redesigned by substituting the second convolution in the bottleneck with parallel grouped convolutions, which enhances feature diversity and multi-scale representation while reducing computational overhead; finally, we optimize the overall architecture and introduce a lightweight down-sampling strategy to mitigate inter-channel interference. Experimental results on the VisDrone2019 dataset demonstrate that the proposed method improves mAP@0.5 and mAP@0.5:0.95 by 2.9 https://github.com/diapfer/LSOYOLO .