Aerial Small Target Detection Based on Improved YOLOv5s Lightweight Algorithm
Aiming at the problems of large number of small and medium-sized target samples,complex target background and little extracted feature information in UAV aerial photography images,an improved YOLOv5s lightweight UAV aerial photography small target detection algorithm was proposed.Firstly,the network structure of the algorithm is improved,and two feature information propagation paths are added to avoid feature loss through cross-level connection.At the same time,the feature information is supplemented by cross-level connection and spatial attention mechanism is added in the feature fusion process to improve the model's attention to small target regions and retain sufficient target feature information.Secondly,according to the characteristics of the data set,the low-level small target detection layer of the backbone network is integrated into the feature pyramid network and the path aggregation network structure,and a small target detection head is added.Finally,SIoU Loss is introduced into the prediction process to further accelerate the model convergence speed and improve the model detection ability and positioning accuracy.The proposed algorithm was tested on the VisDrone2019 dataset,which showed that the improved model mAP50 reached 38.5%,5.9 percentage points higher than that of the baseline method YOLOv5s,and also achieved higher detection accuracy than the that of mainstream detection methods,with better performance for small target detection tasks.
small target detectiondrone imagesYOLOv5scross-level feature fusionmultiscale detection
万方数据
维普
