Small Target Detection Method of Optical Remote Sensing Image Based on Multi-scale Information Fusion.

This paper proposes a multi-scale information fusion based remote sensing small target detection method that aims to address the issue of low target detection accuracy in optical remote sensing images due to complex backgrounds, diversified scales, small targets, and different directions. Firstly, the architecture of the RepConv module significantly increases the detection accuracy of small targets without adding more inference time. Secondly, by introducing the ECA attention mechanism, a C3ECA module is constructed to effectively reduce the interference of complex background areas and achieve accurate positioning of the target area. The PANet structure in YOLOv5 is replaced by the BiFPN structure to balance the feature information of different scales and improve the detection performance of multi-scale objects. In addition, in order to solve the uncertainty of target direction and reduce the boundary discontinuity caused by angle regression, a circular smooth label method is used to provide an effective solution for target detection. The preprocessing method of image slices is employed to successfully achieve the target detection of high-resolution images. This approach greatly minimizes the issue of missed detection and erroneous detection of small objects in large images. The experimental findings indicate that the proposed approach markedly enhances the accuracy of remote sensing image detection and offers notable benefits in the recognition of small-scale targets. [ABSTRACT FROM AUTHOR]