A CNN-Transformer Hybrid Model Based on CSWin Transformer for UAV Image Object Detection

Abstract

The object detection of unmanned aerial vehicle (UAV) images has widespread applications in numerous fields; however, the complex background, diverse scales, and uneven distribution of objects in UAV images make object detection a challenging task. This study proposes a convolution neural network transformer hybrid model to achieve efficient object detection in UAV images, which has three advantages that contribute to improving object detection performance. First, the efficient and effective cross-shaped window (CSWin) transformer can be used as a backbone to obtain image features at different levels, and the obtained features can be input into the feature pyramid network to achieve multiscale representation, which will contribute to multiscale object detection. Second, a hybrid patch embedding module is constructed to extract and utilize low-level information such as the edges and corners of the image. Finally, a slicing-based inference method is constructed to fuse the inference results of the original image and sliced images, which will improve the small object detection accuracy without modifying the original network. Experimental results on public datasets illustrate that the proposed method can improve performance more effectively than several popular and state-of-the-art object detection methods.