Fusion of multi-scale and context for small target detection algorithm of unmanned aerial vehicle rescue

Aiming at the problem of insufficient feature information contained in small targets under unmanned aerial vehicle (UAV) images that led to insufficient detection accuracy of the model, a small target detection algorithm for UAV sea rescue images that integrated multi-scale and contextual information was proposed. Firstly, context enhancement module was designed for small target feature information, which effectively enhanced the ability of the model to process small targets by enhancing the contextual information of the feature layer. Secondly, to improve the robustness of the model, spatial attention module was designed to enhance the learning of important features. Finally, balance L1 loss was used to optimize the loss function of the baseline algorithm and enhance the stability of the model during the process of detection. Based on the Tiny-Person dataset, through extensive experimental comparison with the benchmark algorithm, the proposed algorithm improves the detection performance of small targets on the sea surface by 2.06% on AP50_tiny, which has a positive impact on rescue operations.