%0 Journal Article %A HUANG Guoxin %A LI Wei %A ZHANG Bihao %A LIANG Binbin %A HAN Xiaodong %A GONG Jianglei %A WU Changqing %T Improved SSD-Based Multi-scale Object Detection Algorithm in Airport Surface %D 2022 %R 10.3778/j.issn.1002-8331.2010-0110 %J Computer Engineering and Applications %P 264-270 %V 58 %N 5 %X Aiming at the problem that the existing general object detection method based on deep learning is difficult to detect the airport scene environment object with large scale difference, especially small targets are difficult to detect. This paper proposes a multi-scale object detection algorithm based on SSD algorithm combined with feature pyramid fusion network. The algorithm first uses ResNet-50 as the backbone network, and separately designs six additional feature layers. Secondly, the feature pyramid network is used for feature fusion to obtain more robust semantic information. Finally, Soft-NMS is used to solve the missing detection situation, and the scale ratio of the prior frame is adjusted to better detect small objects. Experiments on the airport surface dataset show that the improved algorithm can achieve 86.31% mAP when the inferred speed is 32?frame/s, which is at the leading level compared with other advanced detectors. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2010-0110