Small target detection in UAV view based on improved YOLOv8 algorithm

The main challenges faced when detecting targets captured by UAVs include small target image size, dense target distribution, and uneven category distribution.In addition, the hardware limitations of UAVs impose constraints on the size and complexity of the model, which may lead to poor detection accuracy of the model. In order to solve these problems, a small target detection method based on the improved YOLOv8 algorithm for UAV viewpoint is proposed. The following improvements are made to the YOLOv8n model. Firstly, a bi-directional feature pyramid network (BiFPN) is introduced to enhance the fusion capability of the features. This improvement leads to better detection of small targets. Secondly, in the head part of the model, the original C2f module is replaced with the C3Ghost module. This change maintains the model's performance while significantly reducing the computational load. Lastly, the detection head adds a channel attention mechanism. This mechanism helps in filtering out unimportant information and enhancing the recognition of key features. The MPDIoU (Minimum Point Distance based IoU) loss function is improved, and the idea of inner-IoU loss function is adopted as a way to improve the model's learning ability for difficult samples. Experimental results on the VisDrone dataset show that the YOLOv8n model with these improvements improves 17.2%, 10.5%, and 16.2% in mean accuracy (mAP), precision (P), and recall (R), respectively, and these improvements significantly improve the performance of small target detection from the UAV viewpoint.