Target recognition and fast tracking neural networks under short term

DOI:	10.11809/bqzbgcxb2025.03.023
Keywords:	target recognition; target tracking; location prediction; deep learning; machine vision
Abstract:	Aiming at the problems of large series structure, low integration and computational efficiency of the full scale target recognition and tracking network under the multi modal detection task, overcoming the dependence of the tracking algorithm on the target motion duration and trajectory accumulation, and facing the requirements of unmanned aerial vehicles, intelligent cruise missiles and other unmanned equipment for multi modal network integration, lightweight and fast response, a neural network for object recognition and fast tracking under short term is proposed. Using adjacent two frames of images as inputs, the correlation features between target semantic information and motion trend information are fused. By the design of sharable feature extraction network, the structural complexity of multi modal object detection networks is reduced. Using a dual branch inference network with static and dynamic links, the target recognition in current and position prediction in future are completed simultaneously. The experimental results show that the proposed algorithm achieves an accuracy rate of 95.4% for object recognition and 909% for location prediction, and capable of endowing intelligent weapons with efficient target recognition and fast tracking computing capabilities with low computing power support.
Issue:	Vol. 46 No. 3 (2025)
Published:	2025-03-31
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