DESIGN OF AUTOMATIC GARBAGE SORTING BIN FOR NON-RESIDENTIAL AREA BASED ON YOLO v5

WANG Wensheng; NIAN Chengxu; ZHANG Chao; YAN Rupeng; WU Xinquan; ZHANG Xinbo

doi:10.13205/j.hjgc.202203024

Volume 40 Issue 3

Mar. 2022

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2022 > 40(3): 159-165

WANG Wensheng, NIAN Chengxu, ZHANG Chao, YAN Rupeng, WU Xinquan, ZHANG Xinbo. DESIGN OF AUTOMATIC GARBAGE SORTING BIN FOR NON-RESIDENTIAL AREA BASED ON YOLO v5[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(3): 159-165. doi: 10.13205/j.hjgc.202203024

Citation:

WANG Wensheng, NIAN Chengxu, ZHANG Chao, YAN Rupeng, WU Xinquan, ZHANG Xinbo. DESIGN OF AUTOMATIC GARBAGE SORTING BIN FOR NON-RESIDENTIAL AREA BASED ON YOLO v5[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(3): 159-165. doi: 10.13205/j.hjgc.202203024

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DESIGN OF AUTOMATIC GARBAGE SORTING BIN FOR NON-RESIDENTIAL AREA BASED ON YOLO v5

doi: 10.13205/j.hjgc.202203024

1. Mechanical and Electrical Engineering School, Beijing Information Science and Technology University, Beijing 100192, China;
2. School of Automation, Beijing Information Science and Technology University, Beijing 100192, China

Received Date: 2021-09-25
Available Online: 2022-07-07

Abstract

Abstract

A design of automatic garbage sorting bin based on model YOLO v5 was proposed, and applied to public places in non-residential communities (such as railway stations, bus stations, shopping malls, schoolyards, etc.). The trash bin was designed with 4 trash cans, arranged in two rows and two columns, with a rotating shaft in the middle, which drove the temporary storage drawer of garbage above the shaft to rotate. The monocular camera was used to collect video images, the embedded chip, Jetson nano by NVIDIA was used as the host computer's main control chip, and the YOLO v5 deep learning model was used for automatic garbage extraction and identification, and the recognition result information of the host computer was sent to the control board of the lower computer, Arduino, through the serial port. The control board, Arduino, controlled the motor to drive the opening of the temporary storage drawer to rotate to the top of the corresponding trash can, and then controlled the lifting platform to dump the trash and complete the automatic classification of the trash. The test results showed that the garbage identification results were stable and reliable, with an accuracy rate of 97%, thus the automatic garbage classification was realized.
- garbage classification,
- YOLO v5,
- target recognition,
- deep learning

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References(21)

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