Small object detection algorithm based on high-resolution image processing and fusion of different scale features

To address the issue of misidentifying and omitting small targets when using UAV high-resolution aerial photos for small-target detection tasks, as a resul7 of the limited percentage of small target pixels and vulnerability to background noise interference. This paper introduces a small target detection algorithm based on high-resolution image processing and fusion of different scale features. ObjectBox is adopted as the baseline network. Firstly, the High-Resolution Image Processing module (HRIP) is introduced to extract the spatial and edge features of small targets. Secondly, the LeakyRelu activation function is used in the ordinary convolution, so that the network can maintain a specific response in the negative range and maintain the gradient in the small range of eigenvalues. Finally, the Bidirectional Feature Pyramid Network (BIFPN) is used to realize the multi-branch different scale feature fusion to alleviate the mutual occlusion problem due to the dense distribution of the small targets, and to improve the model's ability to locate the bounding box of the small targets accurately. Experiments on the VisDrone2019 dataset prove that by enhancing the baseline model, the mean average precision of object detection has reached 37.2% and the accuracy of small object detection has reached 28.7%.