Research on an improved small target detection algorithm for YOLOv8

In order to solve the problems of missed detection and low detection accuracy due to the changes in the shape, appearance and position of objects in the imaging process, as well as the complex influence of lighting conditions and occlusion factors, an improved YOLOv8 algorithm based on Swin Transformer was proposed. By introducing modules including Focus, deeply separable convolutional DwConv, c2, etc., the computation and parameters are reduced, the receptive field and feature channels are increased, and the Swin Transformer module is used to extract visual features to capture the context information of small target objects to enhance the feature representation. In addition, the loss function of the original network is replaced by the WIOU loss function to optimize the model and improve the accuracy of small target detection. Comparative experiments based on public datasets show that compared with the YOLOv8 algorithm, the improved model has improved the accuracy of small target detection P, recall R and average accuracy of mAP@0.5, which enhances the ability of intelligent robots to identify small targets in complex environments and provides important support for the technological progress of related industries.