A Size-Grading Method of Antler Mushrooms Using YOLOv5 and PSPNet

Abstract

Quality grading in antler mushroom industrial production is a labor-intensive operation. For a long time, manual grading has been used for grading, which produces various problems such as insufficient reliability, low production efficiency, and high mushroom body damage. Automatic grading is a problem to be solved urgently for antler mushroom industrial development with increasing labor costs. To solve the problem, this paper deeply integrates the single-stage object detection of YOLOv5 and the semantic segmentation of PSPNet, and proposes a Y-PNet model for real-time object detection and an image segmentation network. This article also proposes an evaluation model for antler mushroom’s size, which eliminates subjective judgment and achieves quality grading. Moreover, to meet the needs of efficient and accurate hierarchical detection in the factory, this study uses the lightweight network model to construct a lightweight YOLOv5 single-stage object detection model. The MobileNetV3 network model embedded with a CBAM module is used as the backbone extractor in PSPNet to reduce the model’s size and improve the model’s efficiency and accuracy for segmentation. Experiments show that the proposed system can perform real-time grading successfully, which can provide instructive and practical references in industry.

Keywords

• antler mushroom
• deep learning
• real time
• size grading