在露天采矿中,无人采矿卡车使用轻量级障碍物检测系统
Guangwei Liu1, Jian Lei2, Zhiqing Guo1
1College of Mining, Liaoning Technical University, Fuxin, 123000, Liaoning, China.
Scientific reports
|March 17, 2025
概括
本研究引入了一种轻量级的YOLOv8模型,用于无人采矿卡车检测,实现高精度 (76.9%) 和缩小尺寸. 优化的模型在开放式采矿应用中平衡了性能和效率.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 现有的无人采矿卡车检测网络在平衡模型大小和准确性方面面临挑战.
- 目前的模型往往不适合在露天采矿中发现的特定设备和环境.
研究的目的:
- 开发一个轻量级和准确的车辆检测算法,用于开采矿的无人采矿卡车.
- 解决现有模型在尺寸,精度和适合采矿设备方面的局限性.
主要方法:
- 提出了一个轻量级的YOLOv8模型,将FasterNet_t0作为骨干网络.
- 用双向特征金字塔网络 (BiFPN) 替换了YOLOv8的部,以优化特征融合.
- 集成动态头具有自我注意机制,并结合SIoU和正常化的高斯瓦瑟斯坦距离 (NWD) 损失功能.
- 应用基于振幅的层适应性稀疏修剪算法 (LAMP) 进行进一步的模型压缩.
主要成果:
- 改进的YOLOv8模型实现了76.9%的检测准确度,超过了类似模型的10%以上.
- 模型尺寸被缩小到与具有可比精度的模型大约20%左右.
- 废弃实验证实了每个提议的改善策略的有效性.
结论:
- 拟议的轻量级YOLOv8模型有效地平衡了无人采矿卡车应用的检测精度和模型大小.
- 整合FasterNet_t0,BiFPN,动态头和优化损失功能显著提高了模型性能和效率.
- 此外,LAMP算法还为模型压缩做出了贡献,而不会影响检测能力.
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