优化深度学习视觉系统,用于从无人机图像中识别人类行为
Hussein Samma1, Ali Salem Bin Sama2
1SDAIA-KFUPM Joint Research Center for Artificial Intelligence (JRC-AI), King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.
Multimedia tools and applications
|June 26, 2023
概括
本研究介绍了一种轻量级的计算机视觉系统,使用优化的SqueezeNet骨干和双层粒子群优化器 (TLPSO) 来有效地识别来自无人机图像的人类行为. 该系统实现了七倍的速度增加,而不会损失精度.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 优化算法 优化算法
背景情况:
- 像YOLO这样的深度学习视觉系统经常使用计算上昂贵的骨干网络 (例如ResNet,Inception).
- SqueezeNet提供了一个压缩的替代方案,但被训练用于广泛的对象分类,而不是特定的动作识别.
- 优化特征提取对于高效,硬件有限的视觉系统至关重要.
研究的目的:
- 开发一种轻量级的视觉系统,用于从无人机图像中识别人类行为.
- 优化SqueezeNet骨干,使用一种新的算法来提高效率和准确性.
- 评估系统在识别步行和跑步行为方面的表现.
主要方法:
- 集成一个双层粒子群集优化器 (TLPSO) 与YOLO和Squeeze.Net.
- 使用TLPSO来减少SqueezeNet用于人类行动识别的卷积过器.
- 数据集包括300个无人机图像 (100个跑步,200个行走) 在各种条件下.
主要成果:
- TLPSO将SqueezeNet过器减少了52%,从而使检测速度增加了七倍.
- 获得了94.65%的F1得分,推断时间为0.061毫秒.
- 与PSO和RLMPSO相比,TLPSO表现出更好的趋同性和适应性.
结论:
- 拟议的轻量级视觉系统有效地识别无人机录像中的人类行为,准确性和速度高.
- TLPSO是一种高效的算法,用于优化轻量级网络中的卷积过器.
- 该系统的性能优于以前基于无人机的人类行为识别的方法.
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