Research on a random sampling method for bulk grain based on the M-Unet and SGBM algorithms

In response to the challenge of limited real-time capability in identifying and positioning sampling points during random sampling of bulk grains in industrial settings, this paper introduces a novel approach. It combines the utilization of a lightweight neural network model, known as M-Unet, with the Semi-Global Block Matching (SGBM) algorithm to achieve the random sampling of bulk grains. The process commences with the deployment of a binocular camera to capture image data from the grain loading area, followed by binocular correction. Recognizing the constraints associated with the traditional U-Net network model, characterized by large parameters and suboptimal real-time performance, we introduce the M-Unet network, which is a more efficient alternative. M-Unet incorporates MobileNet as a substitute for the U-Net network encoder, resulting in a significant improvement in real-time performance and segmentation quality. Subsequently, the M-Unet network is employed to partition the corrected left image into multiple regions based on predefined sampling principles, and random sampling points are generated within each region. To determine the three-dimensional spatial positioning of these sampling points, we employ the SGBM algorithm, a semi-global stereo matching technique, on the left and right images of the corrected grain loading area. Experimental results demonstrate that the proposed method exhibits robust real-time performance and effectively recognizes grain loading areas. Furthermore, this method is suitable for deployment on portable devices, offering novel insights and methodologies for automating and advancing the intelligence of grain sampling processes. This innovation effectively addresses the challenges associated with low efficiency and human-related factors in traditional grain sampling. The findings presented in this paper carry substantial significance in safeguarding the interests of grain farmers, reducing food losses, minimizing waste, and enhancing the intelligence of sampling processes.