Paper
7 December 2023 MDET: a GAN-based target detection algorithm
Qingtian Shen, HaoSen Rong, ZhaoZheng Wang, ZhengJia Han, Zihui Jin, Wen Zhou, Qiuyue Gao, Shangzhong Jin
Author Affiliations +
Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129410L (2023) https://doi.org/10.1117/12.3011966
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China
Abstract
Target detection is a key computer vision technology that is utilized extensively in crucial sectors including aerospace and aviation as well as fine-grained security detection. One of the most frequent problems in computer vision is the implementation of adversarial training in vision algorithms. The target detection technique we present in this research is an adversarial machine learning-based approach that uses GANs. The images are inputted and sent to the GAN network and the detection network, respectively, and then the images produced by the GAN learning are blended with the real images and sent to the target detection network. This algorithm uses GAN as an adversarial trainer for the baseline target detection algorithm. Finally, YOLOv5s is selected for experimental validation, and the data of VOC hybrid 2007 and 2012 are selected for experiment, and the data in the Based on YOLOv5s, its map0.5 and map0.95 have a good improvement in detection effect compared to the original YOLOv5s, and finally based on the comparison experiments and ablation experiments, it shows that the GAN-based target detection algorithm out of this paper is effective.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Qingtian Shen, HaoSen Rong, ZhaoZheng Wang, ZhengJia Han, Zihui Jin, Wen Zhou, Qiuyue Gao, and Shangzhong Jin "MDET: a GAN-based target detection algorithm", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129410L (7 December 2023); https://doi.org/10.1117/12.3011966
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KEYWORDS
Detection and tracking algorithms

Data modeling

Adversarial training

Target detection

Education and training

Gallium nitride

Performance modeling

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