PCCS-YOLOv8: An Enhanced Lightweight Detector for Small UAV Detection in Complex Scenes

Small unmanned aerial vehicles (UAVs) often occupy only a limited number of pixels in an image and can be easily confused with surrounding objects in cluttered scenes, which makes reliable detection difficult. To address this challenge, we develop PCCS-YOLOv8, an enhanced object detector tailored to small UAV targets. A P2 prediction branch is added to retain fine spatial information associated with tiny objects. The cross-stage partial pyramid convolution (CSPPC) module is introduced to offset the additional computational burden caused by the detection branch with high resolution, while the spatial pyramid pooling with efficient layer aggregation network (SPPELAN) combines multiscale pooling with efficient feature aggregation. The convolutional block attention module (CBAM) is further integrated to emphasize features related to targets and reduce interference from complex backgrounds. Experiments were conducted on a UAV dataset containing 7785 images collected from TIB-UAV, Anti-UAV, and self-collected sources. PCCS-YOLOv8 achieved an mAP@0.5 of 94.0% and an mAP@0.5:0.95 of 50.6%, outperforming the YOLOv8 baseline by 2.9 and 2.2 percentage points, respectively. After training, the model was exported, converted to RKNN format, and then deployed on an Orange Pi 5 Pro development board. In real-world detection tests, the embedded system achieved an average frame rate of 27.7 FPS and an average runtime of 46.3 ms per frame. These results demonstrate the potential of the proposed method for real-time UAV detection on edge devices.