Detection Method of Material Drop Point in Compartment Area Based on Improved YOLO v8n-pose

Abstract:

The current method for loading material onto vehicles in silage harvesters, which primarily involves manually controlling the rotation of the throwing arm, is labor-intensive and has high operational requirements. This method not only affects the harvesting efficiency but also easily causes losses of silage. A detection method for determining whether silage fell into the trailer hopper was proposed. Firstly, an improved YOLO v8n-pose model was constructed. By introducing the lightweight coordinate attention (LCA), DynamicConv, and Deformable Convolution v4 (DCNv4), the detection accuracy of the trailer hopper corners and the falling points of silage was improved. After coordinate transformation, combined with the convex hull algorithm and cross product method, it was determined whether the silage fell into the trailer hopper. Experiments proved that the improved YOLO v8n-Pose model achieved a mAP50:95 of 95.1% in the loose state of the silage flow, an increase of 5.9 percentage points compared with that of the original model, and a mAP50:95 of 95.0% in the normal state of the silage flow, an increase of 3.3 percentage points compared with that of the original model. The improved model demonstrated higher detection accuracy and stability in different states of the silage flow, with a significant enhancement in adaptability to abnormal working conditions with drastic shape changes, which provided a solid visual foundation for future adaptive throwing control.