Improved YOLOv5-based small object detection method for UAVs

Many factors cause false or missed detection of small UAV objects, such as large changes in object scale due to illumination, dense objects, complex backgrounds and occlusions that lead to low model detection accuracy. To solve the above problems, an improved UAV small object detection method is proposed, based on the YOLOv5.Replace the original conv2d detection head with Adaptive Spatial Feature Fusion, add Attentional Convolutional Mixtures, and replace the original regression loss function with F-EIOU. Extensive experiments are conducted on the Visdrone2019 dataset. The experimental results show that the improved YOLOv5 increases the mAP@0.5 by 6.1%, the mAP@0.5:0.95 by 2.7%, the recall by 5.7% and the precision by 3.2% on the Visdrone2019 dataset, meeting the practical needs of UAV small object detection in complex scenarios.