This study aims to solve the problem of material detection and shovel difficulty judgment of earth moving machinery and characterize the material shovel difficulty by using radar chart method comprehensively. YOLOv4 object detection network is used for material detection, and the average detection accuracy reaches 93.03%. We fuse deep learning technology and radar chart method information and explore the correlation between material detection and material shoveling ease assessment. The evaluation of the identifiable shovel difficulty of the material is achieved with vision sensors, and the method is real-time and efficient. This is an incipient attempt. The detailed information provided in this study is valuable for the autonomous shoveling of loaders.

本研究旨在解决土方机械物料检测和铲挖难度判断问题，综合利用雷达图法表征物料铲挖难度。材料检测采用YOLOv4物体检测网络，平均检测准确率达到93.03%。我们融合深度学习技术和雷达图方法信息，探索材料检测与材料铲除难易评估之间的相关性。通过视觉传感器实现对物料可识别铲挖难度的评估，该方法实时高效。这是一个初步的尝试。本研究中提供的详细信息对于装载机的自动铲挖很有价值。