Research on Tank Target Detection in Complex Background based on Deep Learning Algorithm

Ming Lei ； Yanjie Liu ； Bohong Zou ； Kaige Huang ； Xuan Guo

《International Core Journal of Engineering》 9卷1期 (2023/01) Pp. 88-98

https://doi.org/10.6919/ICJE.202301_9(1).0013

摘要

In a complex environment, it is very difficult to detect the target, and the target is easily affected by human beings or environment. In this paper, the algorithm Faster RCNN convolution neural network (FAHL‐Convolution Neural Network) based on deep learning is adopted by transfer learning method to solve this problem. In military activities, tank targets are camouflaged and hidden in the jungle. In order to improve detection accuracy and reduce the rate of missed detection, Faster RCNN algorithm is combined with RPN network to achieve the effect of feature fusion, and the candidate areas are optimized, which can not only improve the detection accuracy but also ensure the speed. Experimental results show that the improved algorithm has higher detection accuracy and robustness.

關鍵字

Target Detection ； Complex Environment ； Convolutional Neural Network ； Area of Candidate

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