Research on the Method for Recognizing Bulk Grain-Loading Status Based on LiDAR

TL;DR

This paper introduces a LiDAR-based method to automatically detect and monitor grain loading in vehicles, improving safety and efficiency over traditional manual methods.

Contribution

A novel LiDAR-based method with deep learning for recognizing bulk grain-loading status in vehicles is proposed.

Findings

The point cloud classification task achieved 97.9% overall accuracy and 98.1% mean accuracy.

Vehicle component segmentation reached 99.1% overall accuracy and 96.6% Mean Intersection over Union.

Abstract

Grain is a common bulk cargo. To ensure optimal utilization of transportation space and prevent overflow accidents, it is necessary to observe the grain’s shape and determine the loading status during the loading process. Traditional methods often rely on manual judgment, which results in high labor intensity, poor safety, and low loading efficiency. Therefore, this paper proposes a method for recognizing the bulk grain-loading status based on Light Detection and Ranging (LiDAR). This method uses LiDAR to obtain point cloud data and constructs a deep learning network to perform target recognition and component segmentation on loading vehicles, extract vehicle positions and grain shapes, and recognize and make known the bulk grain-loading status. Based on the measured point cloud data of bulk grain loading, in the point cloud-classification task, the overall accuracy is 97.9% and the mean accuracy is 98.1%. In the vehicle component-segmentation task, the overall accuracy is 99.1% and the Mean Intersection over Union is 96.6%. The results indicate that the method has reliable performance in the research tasks of extracting vehicle positions, detecting grain shapes, and recognizing loading status.

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