G-YOLO:基于YOLOv7的轻型危险化学品车辆的目标检测算法
Cuiying Yu1, Lei Zhou1, Bushi Liu1
1Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China.
本研究引入了危险化学品车辆的轻量级物体检测模型,提高了运输过程中的安全性. 改进的YOLOv7微型模型提供了更少参数的准确检测,降低了事故风险.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 运输安全运输安全
背景情况:
- 危险化学品车辆运输危险物质,造成火灾,爆炸和泄漏的风险.
- 确保危险物质运输期间的安全对于人类和环境保护至关重要.
- 现有的物体检测方法在这个领域的实时应用中可能缺乏效率.
研究的目的:
- 开发一种轻量级和高效的物体检测方法,用于危险的化学载体.
- 提高检测危险化学载体的准确性和稳定性.
- 为了减少对象检测模型的计算负担和参数数量.
主要方法:
- 在YOLOv7小型模型的车和子中使用了轻量级的特征提取结构 (E-GhostV2网络).
- 在模型的骨干中内置部分卷积 (PConv),以减少计算和内存访问.
- 采用WIoU损失函数来平衡高质量和低质量样本的培训,增强概括性.
主要成果:
- 拟议的方法实现了对危险化学载体的满意的检测准确性.
- 与基线相比,改进的模型显著减少了模型参数的数量.
- 观察到提高了效率和特征提取能力.
结论:
- 轻量级物体检测模型为识别危险化学载体提供了强大的解决方案.
- 该方法为提高危险物质运输的安全性和理论研究提供了实际支持.
- 优化的模型平衡了检测性能和计算效率.
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