An improved YOLOv5-based method for robotic vision detection of grain caking in silos

Detecting and cleaning grain caking on the inner walls of silos is an important task to ensure food safety in storage facilities. However, in response to challenges such as insufficient lighting conditions, small and diverse forms of grain caking, this paper proposes the development and evaluation of a convolutional neural network model for robot vision detection of grain caking. The following improvements to the visual detection algorithm based on YOLOv5 are proposed in this article. Firstly, the Convolutional Block Attention Module (CBAM) and the improved Total Cross Union (CIoU) loss function are introduced to enhance the detection accuracy of grain caking. Secondly, by adding the Retinex Net algorithm with dark light enhancement, the recognition and detection performance under low light conditions can be improved. The improved YOLOv5 algorithm was trained and validated on a custom grain caking dataset. Comparative experiments show that compared with existing detection architectures, the improved algorithm has improved the average accuracy of grain caking detection by 1.8 % to 3.8 %. Finally, the improved algorithm proposed in this article was deployed on a wall climbing robot based on negative pressure adsorption, achieving real-time detection and automatic cleaning of grain caking.