Affiliation:
1. Liaoning Provincial Key Laboratory of Intelligent Manufacturing and Industrial Robots, Shenyang University of Technology, Shenyang 110870, China
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.
Funder
Liaoning Provincial Education Department Project
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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