MFE-YOLOX:Dense small target detection algorithm under UAV aerial photography
A mixed feature enhancement(MFE)method is proposed for the problem that the object scale varies greatly during UAV aerial photography,and most of the detection targets are small and dense objects.First,an attention mecha-nism is added to the super-resolution method to enhance small target information extraction;then,a new fusion calculation method of feature layers is proposed to enhance the fusion efficiency between different feature layers and improve the detec-tion accuracy of small and medium-sized targets.Finally,a tail-end perceptual field expansion layer is designed to expand the tail-end feature layer perceptual field so that the detection head can receive rich object information to locate and distin-guish dense objects.The experiments are tested on the test set of dataset VisDrone2021,and the results show that the AP50 result using the MFE-YOLOX network is 47.78％,and the accuracy is improved by 9.43％with similar number of parame-ters and computational load compared to the original network.
small object detectionunmanned aerial vehicleattention mechanismfeature fusionYOLOX
万方数据
维普
