Method for Fruit and Vegetable Automatic Recognition Based on Residual Block and Attention Mechanism

被引：0
作者：
Yu Q. [1 ]
Zhang R. [1 ,2 ]
Li D. [1 ]
Yun Y. [1 ,3 ]
Wang Z. [1 ,3 ]
机构：
[1] College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao
[2] College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin
[3] Shandong Collaborative Innovation Center of Major Crop Mechanized Production Equipment, Qingdao Agricultural University, Qingdao
来源：
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2023年 / 54卷
关键词：
attention mechanism; fruits and vegetables recognition; residual block; YOLO v5;
D O I：
10.6041/j.issn.1000-1298.2023.S2.025
中图分类号：
学科分类号：
摘要：
To solve the problems of low efficiency and high cost in fruits and vegetables recognition, a fruit and vegetable recognition model based on residual block and attention mechanism was proposed, and successfully deployed in fruit and vegetable intelligent recognition equipment. The fruit and vegetable automatic recognition device was composed of Raspberry Pi, STM32F103ZET6, camera, weighing sensor, processor, display screen, micro printer, binding machine and power supply. The central controller interacted with the display screen to display various parameters in real time. The image and quality of the object to be measured were collected through the camera and weighing sensor. The fruit and vegetable automatic recognition model deployed in the Raspberry Pi could accurately identify the fruits and vegetables. At the same time, it cooperated with MCU STM32F103ZET6 to print fruit and vegetable related information and control the tying machine to seal and pack. Based on YOLO v5 network, an automatic recognition model RB + CBAM - YOLO v5 was constructed by adding residual blocks and attention mechanism. The network was trained with the self-made data set, and six kinds of networks were compared, and the optimal network was selected for the device side detection test. The experimental results showed that the accuracy rate, recall rate and mAP0.5 of RB + CBAM - YOLO v5 were 83. 55%, 96.08% and 96.20%, respectively, which were 4.47 percentage points, 1. 10 percentage points and 0.90 percentage points higher than those of YOLO v5. The RB + CBAM - YOLO v5 model was deployed in the embedded device Raspberry Pi, and the device could realize accurate identification, automatic weighing, printing slip and fast packaging functions, which could meet the needs of fruits and vegetables identification and unsold devices. © 2023 Chinese Society of Agricultural Machinery. All rights reserved.
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页码：214 / 222
页数：8
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