Remote Sensing Image Object Detection Algorithm Based on Multi-branch Feature Mapping

Abstract

Due to the complex background, small and dense targets, and large scale continuous changes in remote sensing images, universal object detectors are difficult to adapt well, resulting in poor detection performance. To address the above issues, a multi-branch feature mapping based remote sensing image object detection algorithm is proposed based on the YOLOv5s model. Firstly, a RepVGG module combined with gated channel transformation is designed using structural reparameterization technology. Its series structure is used to replace the C3 module of the original backbone network, aggregating global contextual information and enhancing feature expression and extraction capabilities. Secondly, the adaptive exponential weighted pooling method and the sampling method of inverse process reconstruction feature fusion network are used to maximize the retention of feature information and improve the detection performance of smaller targets. Finally, the combination of EIoU and Focal Loss is introduced as the loss function of the model to optimize the regression rate of the prediction box and the loss weight distribution of difficult and easy samples, further improving the positioning accuracy. The experimental results on the DIOR and NWPU VHR-10 datasets show that the average accuracy of the proposed algorithm reaches 92.2% and 92.5%, respectively, which is 3.5 percentage points and 5.6 percentage points higher than YOLOv5s, achieving better detection performance. At the same time, the real-time performance also meets the requirements of remote sensing image object detection in actual scenes.

Keywords

• remote sensing images; structural reparameterization; gated channel transformation; sampling method; loss weight allocation