A RECYCLABLE WASTE SORTING SYSTEM BASED ON AN IMPROVED INCEPTION RESNET V2 NETWORK

XU Li; ZHOU Lawu; LI Gaojia

doi:10.13205/j.hjgc.202404027

Volume 42 Issue 4

Apr. 2024

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2024 > 42(4): 233-241

XU Li, ZHOU Lawu, LI Gaojia. A RECYCLABLE WASTE SORTING SYSTEM BASED ON AN IMPROVED INCEPTION RESNET V2 NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(4): 233-241. doi: 10.13205/j.hjgc.202404027

Citation:

XU Li, ZHOU Lawu, LI Gaojia. A RECYCLABLE WASTE SORTING SYSTEM BASED ON AN IMPROVED INCEPTION RESNET V2 NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(4): 233-241. doi: 10.13205/j.hjgc.202404027

XU Li, ZHOU Lawu, LI Gaojia. A RECYCLABLE WASTE SORTING SYSTEM BASED ON AN IMPROVED INCEPTION RESNET V2 NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(4): 233-241. doi: 10.13205/j.hjgc.202404027

Citation:

XU Li, ZHOU Lawu, LI Gaojia. A RECYCLABLE WASTE SORTING SYSTEM BASED ON AN IMPROVED INCEPTION RESNET V2 NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(4): 233-241. doi: 10.13205/j.hjgc.202404027

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A RECYCLABLE WASTE SORTING SYSTEM BASED ON AN IMPROVED INCEPTION RESNET V2 NETWORK

doi: 10.13205/j.hjgc.202404027

School of Electrical and Information Engineering, Changsha University of Technology,Changsha 410114, China

Received Date: 2023-05-17
Available Online: 2024-06-01

Abstract

Abstract

Garbage siege has always been a big problem in China’s urban management. Aiming at the difficulties of the recyclable waste disposal process, this paper proposed a system based on the improved Inception ResNet V2 network combined with the ROBOT MG400 robotic arm for automatic sorting of recyclable waste. Firstly, we improved the fixture on the MG400 robotic arm to make it more suitable for garbage grabbing. Then, we independently created a dataset of 50850 sheets, based on which the garbage images were processed by background noise reduction, image classification, and voting algorithms, and the CBAM attention mechanism was added to the output layer of the Inception ResNet V2 network to improve the accuracy of model recognition. Finally, the whole system was experimentally verified. The verification results showed that the system could classify garbage more accurately and collect it into the corresponding garbage collection container, and the recognition accuracy was 99.35% and 95.39% when the conveyor belt was stationary and running, the mAP value of the improved network was 2.56% higher than that of the original model, and the sorting efficiency of the system reached 60 pieces per minute. Therefore, this system can independently complete the sorting of recyclable waste with high efficiency, high accuracy, and high precision.
- Inception ResNet V2,
- image classification,
- recyclables,
- sorting systems,
- attention mechanisms

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References(35)

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