Unmanned aerial vehicle (UAV) images have characteristics of high resolution, large field of vision and small target. However, existing object detection methods are generally insufficient in extracting the features of these small targets. Aiming at this problem, a small target detection algorithm is proposed in this paper. First, in order to improve the ability of feature expression for small targets, CenterNet, a detection network which uses center points to represent small targets, is adopted, and a deformable dual attention mechanism is induced. Then on this basis, for the problem of deficiency of original nonmaximum suppression (NMS) in dealing with nested redundant frames, we propose to use a generalized non-maximum suppression (G-NMS) in the process of redundancy detection elimination. Finally, LegoNet convolution unit is introduced to reduce convolution parameters and achieve balance between precision and velocity. The main validation data sets used in this paper are Visdrone 2019 and UAV_ OUC. Images in UAV_OUC have higher resolution than those in VisDrone2019. Compared with CenterNet, the detection accuracies of UAV_OUC and VisDrone2019 are improved by about 10% and 2% respectively.
WANG Shengke, REN Pengfei, Lü Xin, ZHUANG Xinfa
. Small Target Detection Algorithm of UAV High Resolution Image Based on Center Point and Dual Attention Mechanism[J]. Journal of Applied Sciences, 2021
, 39(4)
: 650
-659
.
DOI: 10.3969/j.issn.0255-8297.2021.04.012
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