无人机运动趋势增强二维检测研究

Hao Wu
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引用次数: 0

摘要

受人类视觉系统的启发,我们提出了一种基于运动信息的无人机检测增强机制——协同过滤机制(Collaborative Filtering mechanism, CFM)。CFM通过基于循环生成对抗网络(CycleGAN)的基于gan的图像平移来增强小目标特征,并在YOLO-V5s的特征提取级联中过滤掉不相关的特征,从而提高目标检测性能。在实验中,我们在VisDrone数据集上验证了CFM模块带来的性能提升。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Motion Trend Enhanced 2D Detection on Drones
Inspired by the human visual system, we proposed a motion information-based enhancement mechanism for drone detection, named Collaborative Filtering Mechanism (CFM). CFM enhances small object features through GAN-based image translation which is based on a Cycle Generative Adversarial Network (CycleGAN), and filters out unrelated features during the feature extraction cascade of YOLO-V5s, thus improving the performance of object detection. In the experiments, we verified the performance improvement brought by the proposed CFM module on the VisDrone dataset.
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