APPLICATION AND COMPARISON OF DIFFERENT DEEP LEARNING MODELS IN RECOGNITION OF FOOD WASTE TYPES

LIU Zhi; GAO Dongming

doi:10.13205/j.hjgc.202403031

Volume 42 Issue 3

Mar. 2024

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LIU Zhi, GAO Dongming. APPLICATION AND COMPARISON OF DIFFERENT DEEP LEARNING MODELS IN RECOGNITION OF FOOD WASTE TYPES[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(3): 254-260. doi: 10.13205/j.hjgc.202403031

Citation:

LIU Zhi, GAO Dongming. APPLICATION AND COMPARISON OF DIFFERENT DEEP LEARNING MODELS IN RECOGNITION OF FOOD WASTE TYPES[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(3): 254-260. doi: 10.13205/j.hjgc.202403031

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APPLICATION AND COMPARISON OF DIFFERENT DEEP LEARNING MODELS IN RECOGNITION OF FOOD WASTE TYPES

doi: 10.13205/j.hjgc.202403031

LIU Zhi,
GAO Dongming^,

College of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China

Received Date: 2023-02-28
Available Online: 2024-05-31

Abstract

Abstract

Kitchen waste and post-meal waste vary greatly in texture, if the pre-treatment process doesn’t effectively identify the type of kitchen waste, and then take the appropriate working parameters, it often leads to poor treatment effect of the reduction treatment equipment. We collected and processed images of vegetable waste and post-meal waste in different seasons and different dietary styles, considering the differences in images between tailgate waste and post-meal waste, on this basis, ResNet18 was used as the base network, and attention mechanism was introduced to design a new deep learning model for kitchen waste recognition, which was compared with ResNet18, ECANET+ResNet18, SENET+ResNet18, and SANET+ResNet18 models. The results showed that all the above four network models had high accuracy rates. Their accuracy rates were 96.73%, 97.10%, 97.28%, and 96.92%, respectively; their loss rates were 4.35%, 4.11%, 3.76%, and 4.17%, respectively; in terms of training time, ECANET+ResNet18 had the shortest training time, which was 350 seconds faster than ResNet18. ECANET+ResNet18 network effectively improved the performance of ResNet18 network, achieved the highest accuracy rate and the smallest loss rate, and could meet the requirements of machine recognition of kitchen waste.
- food waste,
- deep learning,
- image processing,
- type identification,
- attention mechanism

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References(24)

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