Review on Target Detection of Image Homing Ammunition

doi:10.12382/bgxb.2021.0610

Abstract

Abstract: The onboard image target detection method is the key technology to realize the autonomous attack on the target by the “fire-and-forget” image homing ammunition. At present, the image homing of ammunition is faced with some problems, such as bad imaging environment, rapid change of targets' characteristics, and strict requirements for algorithm volume and speed. Firstly, the target detection methods based on deep learning are divided into methods based on anchor box, methods without anchor box and methods based on transformer, and the main technical progress of various methods is reviewed. Then, the key technologies in onboard image target detection model deployment, such as lightweight feature extraction network, enhancement of feature map for prediction, non-maximum suppression post-processing algorithm, sample equalization in training, and model compression, are studied. Finally, the performances of the typical detection algorithms on ImageNet, COCO and datasets for onboard image are compared, and the possible development in the future is looked into.

Key words: onboardimage, targetdetection, deeplearning, modeldeployment

CLC Number:

`391.4

YANG Chuandong， QIAN Lizhi， XUE Song, CHEN Dong, LING Chong. Review on Target Detection of Image Homing Ammunition[J]. Acta Armamentarii, 2022, 43(10): 2687-2704.

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