Embedded Yolo-Fastest V2-Based 3D Reconstruction and Size Prediction of Grain Silo-Bag

Abstract

Contactless and non-destructive measuring tools can facilitate the moisture monitoring of bagged or bulk grain during transportation and storage. However, accurate target recognition and size prediction always impede the effectiveness of contactless monitoring in actual use. This paper developed a novel 3D reconstruction method upon multi-angle point clouds using a binocular depth camera and a proper Yolo-based neural model to resolve the problem. With this method, this paper developed an embedded and low-cost monitoring system for the in-warehouse grain bags, which predicted targets’ 3D size and boosted contactless grain moisture measuring. Identifying and extracting the object of interest from the complex background was challenging in size prediction of the grain silo-bag on a conveyor. This study first evaluated a series of Yolo-based neural network models and explored the most appropriate neural network structure for accurately extracting the grain bag. In point-cloud processing, this study constructed a rotation matrix to fuse multi-angle point clouds to generate a complete one. This study deployed all the above methods on a Raspberry Pi-embedded board to perform the grain bag’s 3D reconstruction and size prediction. For experimental validation, this study built the 3D reconstruction platform and tested grain bags’ reconstruction performance. First, this study determined the appropriate positions (−60°, 0°, 60°) with the least positions and high reconstruction quality. Then, this study validated the efficacy of the embedded system by evaluating its speed and accuracy and comparing it to the original Torch model. Results demonstrated that the NCNN-accelerated model significantly enhanced the average processing speed, nearly 30 times faster than the Torch model. The proposed system predicted the objects’ length, width, and height, achieving accuracies of 97.76%, 97.02%, and 96.81%, respectively. The maximum residual value was less than 9 mm. And all the root mean square errors were less than 7 mm. In the future, the system will mount three depth cameras for achieving real-time size prediction and introduce a contactless measuring tool to finalize grain moisture detection.

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“…Meanwhile, MAPE, as shown in Equation (14), is used to calculate prediction errors as a percentage. As Equation (15) indicates, MAE measures the error between predicted and actual values, considering tolerance for outliers. Additionally, MSE is used to measure the difference between predicted values and actual values.…”

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