| Citation: | LIU S D，LIU Y H，SUN Y M，et al. Small object detection in UAV aerial images based on inverted residual attention[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（3）：514-524 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0362
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Aiming at the problems of complex background and too many small-size targets in UAV aerial images, a small target detection algorithm based on inverted residual attention is proposed. Firstly, an inverted residual module and an inverted residual attention module are embedded into the backbone network, while rich spatial information and deep semantic information of small targets are obtained by feature information mapping from low dimension to high dimension, thus improving the accuracy of small target detection; Secondly, in feature fusion, a multi-scale feature fusion module is established to fuse the shallow spatial information and deep semantic information, and to generate four detection heads with different sensory fields, which improves the recognition of small-size targets and reduces missed detection of small targets; Finally, a mosaic mixed data enhancement method is designed to establish the linear relationship between the data, increase the complexity of the image background and improve the robustness of the algorithm. The experimental results on data set VisDrone show that the mean average precision of this algorithm is 1.2% higher than that of DSHNet, which means that the proposed algorithm could effectively reduce missed detection and false detection of small targets in UAV aerial images.
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