Improved YOLO v2 for Target Recognition of Armored Vehicles

doi:10.3969/j.issn.1006-2475.2018.09.014

Abstract

Abstract: The technology of military target recognition is an important part of military information processing, which plays an important role in realizing the informatization and intelligentization of military equipment. In recent years, with the wide application of convolutional neural network in image recognition field, a variety of network structures based on image recognition task emerge in an endless stream. So it is of great practical significance and military application value to apply the new technology in military target recognition. Based on the YOLO v2 network which has the best recognition effect at present, this paper redefines the optimal number of anchors and their width and height dimensions by dimension clustering, and makes the armored vehicle data set with obvious features as the target area, so that the network can recognize the armored targets more accurately. Experimental results show that the method can effectively identify the specific armored targets in real time.

Key words: armored target recognition, dimensional clustering, YOLO v2, anchor

CLC Number:

TP391

WANG Shu-guang, LYU Pan-fei. Improved YOLO v2 for Target Recognition of Armored Vehicles[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2018.09.014.

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