基于轻量级YOLOv7-SPSD的河关键部分的检测
1College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
这项研究介绍了YOLOv7-SPSD,这是一种用于机器人河处理的轻量级深度学习模型. 它准确地检测到尾,提高了海鲜行业的自动化效率.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
背景情况:
- 河加工,特别是背部装甲的去除,是劳动密集型,缺乏机械化.
- 高效的机器人处理需要精确识别和定位目标区域.
研究的目的:
- 开发一种轻量级的深度学习模型,用于准确地检测河尾巴.
- 为了使机器人系统能够在河加工中自动移除背.
主要方法:
- 构建了河尾巴数据集,并进行了数据标签.
- 通过将Slimneck,PConv和SimAM集成到YOLOv7-tiny.com中开发了YOLOv7-SPSD.
- 使用DepGraph修剪优化模型,用于边缘设备部署.
主要成果:
- 在0.5值下,YOLOv7-SPSD实现了99.6%的mAP和99.6%的F1得分.
- 该模型在CPU上以7.1 FPS处理,与YOLOv7-tiny.com相比增加了2.7 FPS.
- 观察到GFLOPS (74.6%),参数 (71.6%) 和大小 (8.1 MB) 的显著减少.
结论:
- YOLOv7-SPSD模型是有效的智能河深度处理.
- 这一进步有助于在自动化海鲜加工设备中部署人工智能.
- 介绍了水产养殖行业智能自动化的新概念.
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