YOLOv8-GAIS: improved object detection algorithm for UAV aerial photography

To address the issue of complex backgrounds in dim scenes, which cause object edge blurring and obscure small objects, leading to misdetection and omission, an improved YOLOv8-GAIS algorithm is proposed. The FAMFF (four-head adaptive multi-dimensional feature fusion) strategy is designed to achieve spatial filtering of conflicting information. A small object detection head is incorporated to address the issue of large object scale variation in aerial views. The SEAM (spatially enhanced attention mechanism) is introduced to enhance the network's attention and capture ability for occluded parts in low illumination situations. The InnerSIoU loss function is adopted to emphasize the core regions, thereby improving the detection performance of occluded objects. Field scenes are collected to expand the VisDrone2021 dataset, and the Gamma and SAHI (slicing aided hyper inference) algorithms are applied for preprocessing. This helps balance the distribution of different object types in low-illumination scenarios, optimizing the model's generalization ability and detection accuracy. Comparative experiments show that the improved model reduces the number of parameters by 1.53 MB, and increases mAP50 by 6.9%, mAP50-95 by 5.6%, and model computation by 7.2 GFLOPs compared to the baseline model. In addition, field experiments were conducted in Dagu South Road, Jinnan District, Tianjin City, China, to determine the optimal altitude for image acquisition by UAVs. The results show that, at a flight altitude of 60 m, the model achieves the detection accuracy of 77.8% mAP50.