基于轻量级YOLOv5的水面垃圾检测
1College of Engineering Science and Technology, Shanghai Ocean University, Shanghai, 21306, China. 1055852350@qq.com.
Scientific reports
|March 14, 2024
概括
研究人员开发了一种轻量级YOLOv5算法,用于高效地检测水面垃圾. 这种深度学习方法提高了检测速度和准确性,使其适合在无人船上部署,以打击河流污染.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 机器人技术 机器人技术 机器人技术
背景情况:
- 自20世纪80年代以来,由于经济增长,河流中的塑料垃圾增加带来了环境挑战.
- 手动垃圾回收是低效和高风险,因为大量和运营危险.
- 深度学习,特别是YOLO算法,在环境监测中对象检测具有前景.
研究的目的:
- 开发一种高效,轻量级的深度学习算法,用于检测河流中的表面垃圾.
- 调整YOLOv5算法,用于部署在无人船上进行自动垃圾回收操作.
主要方法:
- 实施了针对水面垃圾检测的轻量级版本的YOLOv5算法.
- 使用Orca数据集来评估性能指标的算法的验证.
- 专注于优化算法以减少参数数量和提高检测速度.
主要成果:
- 改进的YOLOv5算法显示检测速度增加了4.3%.
- 实现了84.9%的平均平均精度 (mAP) 和88.7%的精度.
- 将参数数量减少到原始数据的仅12%,表明显著的轻量化.
结论:
- 与原始相比,轻量级的YOLOv5算法为水面垃圾检测提供了卓越的准确性和速度.
- 该算法的缩小尺寸使其适合在资源有限的硬件上部署,例如无人船.
- 这一进步有助于为河流塑料污染管理提供更有效,更广泛的自动化解决方案.
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