Research on Lightweight Model for Rapid Identification of Chunky Food Based on Machine Vision

Abstract

To meet the demands of the food industry for automatic sorting of block-shaped foods using DELTA robots, a machine vision detection method capable of quickly identifying such foods needs to be studied. This paper proposes a lightweight model that incorporates the CBAM attention mechanism into the YOLOv5 model, replaces ordinary convolution with ghost convolution, and replaces the position loss function with SIoU loss. The resulting YOLOv5-GCS model achieves a mAP increase from 95.4% to 97.4%, and a reduction in parameter volume from 7.0 M to 6.2 M, compared to the YOLOv5 model. Furthermore, the first 17 layers of the MobileNetv3-large network are replaced with the CSPDarkNet53 network in YOLOv5-GCS, resulting in the YOLOv5-MGCS lightweight model, with a high FPS of 83, which is capable of fast identification of block-shaped foods.

Keywords

• deep learning
• YOLOv5 algorithm
• lightweight model
• machine vision