Small target detection based on improved YOLOv8s aerial images

Abstract

Due to the difficulties such as the large proportion of small targets in UAV aerial images, an improved aerial image target detection algorithm is proposed in view of the low performance of the current mainstream algorithms in aerial image target detection tasks, and the high false detection rate and missing rate. First, the RFCBAM convolutional module is introduced into the YOLOv8s backbone network, which enhances the spatial awareness of the model without increasing the computing load. Secondly, the lightweight up-sampling operator CARAFE is introduced, which has a larger perceptual range and can fuse semantic information in a larger receptive field without introducing too many parameters and computing burden. Then, a P2 detection head was added to detect smaller targets, while a P5 detection head was removed for more efficient detection of aerial image targets. Finally, WIOUv3 is introduced as a new regression loss function to improve the learning ability of the model to cope with poor samples. Experiments show that compared with YOLOv8s, the improved model in this paper improves MAP50 by 6.5% and MAP50-90 by 4.4% in Visdrone2019 data set, and the number of parameters decreases by 28.1%.