研究基于改进的YOLOv4的蛋识别算法
1College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China.
British poultry science
|March 11, 2024
概括
这项研究增强了YOLOv4算法,用于在自由放牧场检测小蛋. 改进的YOLOv4-ours模型显著提高了机器人收集卵的检测准确度和速度.
科学领域:
- 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
- 农业技术 农业技术
背景情况:
- 在复杂的自由放牧农场环境中,小蛋很难被检测出来.
- 自动收集蛋需要对小,具有挑战性的目标进行强大的物体检测.
研究的目的:
- 改进用于检测蛋的YOLOv4卷积神经网络.
- 为了提高在自由行环境中的蛋机器人的性能.
主要方法:
- 修改了YOLOv4算法,删除了一个框的尺度.
- 创建了一个专门的蛋数据集来训练改进的模型.
- 实现了我们的YOLOv4算法,用于实时检测.
主要成果:
- 实现了98.85%的精度,96.67%的回忆率和98.60%的平均精度.
- 将F1得分提高到97%,检测时间缩短到每张图像0.20秒.
- 与原来的YOLOv4模型相比,表现出更高的性能.
结论:
- 我们的YOLOv4模型准确地检测到自由行环境中的蛋.
- 改进的算法满足了蛋机器人的实时识别和采摘要求.
- 这一进步支持在农业中有效和自动收集蛋.
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