Lightweight unmanned aerial vehicle object detection algorithm based on improved YOLOv8

With the rapid development of unmanned aerial vehicle (UAV) technology and computer vision, real-time object detection in UAV aerial images has become a current research hotspot. However, the detection tasks in UAV aerial images face challenges such as disparate object scales, numerous small objects, and mutual occlusion. To address these issues, this paper proposes the ASM-YOLO model, which enhances the original model by replacing the Neck part of YOLOv8 with an efficient bidirectional cross-scale connections and adaptive feature fusion (ABiFPN) . Additionally, a Structural Feature Enhancement Module (SFE) is introduced to inject features extracted by the backbone network into the Neck part, enhancing inter-network information exchange. Furthermore, the MPDIoU bounding box loss function is employed to replace the original CIoU bounding box loss function. A series of experiments was conducted on the VisDrone-DET dataset, and comparisons were made with the baseline network YOLOv8s. The experimental results demonstrate that the proposed model in this study achieved reductions of 26.1% and 24.7% in terms of parameter count and model size, respectively. Additionally, during testing on the evaluation set, the proposed model exhibited improvements of 7.4% and 4.6% in the AP50 and mAP metrics, respectively, compared to the YOLOv8s baseline model, thereby validating the practicality and effectiveness of the proposed model. Subsequently, the generalizability of the algorithm was validated on the DOTA and DIOR datasets, which share similarities with aerial images captured by drones. The experimental results indicate significant enhancements on both datasets.