Abstract: Aiming at the shortcomings of Swin Transformer in extracting local feature information and expressing features,this paper proposes a center point target detection algorithm based on improved Swin Transformer to improve its performance in target detection.By adjusting the network structure and introducing a deconvolution module to enhance the network’s ability to extract local feature information,using an adaptive two-dimensional Gaussian kernel and a regression head module to detect the center point of the target,so as to enhance the feature expression ability,and adding a dropout activation function to the Swin Transformer block module to alleviate the network overfitting problem.The improved algorithm is validated on the Pascal VOC and MS COCO 2017 datasets,respectively.The experimental results show that the improved Swin Transformer algorithm achieves an accuracy of 81.1% on the Pascal VOC dataset and 37.2% on the MS COCO dataset,significantly superior to other mainstream object detection algorithms.

Key words: Deep learning, Image processing, Object detection, Deconv, Swin Transformer