Source Jouranl of CSCD
Source Journal of Chinese Scientific and Technical Papers
Included as T2 Level in the High-Quality Science and Technology Journals in the Field of Environmental Science
Core Journal of RCCSE
Included in the CAS Content Collection
Included in the JST China
Indexed in World Journal Clout Index (WJCI) Report
JIE Ya-wei, XU Ran-yun, DING Wei, JIANG Yi-heng, ZHANG Ben, LIU Hong-yuan. AOX FORMATION DURING THE ADVANCED OXIDATION OF PHENOL WASTEWATER CONTAINING CHLORIDE ION[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(5): 1-8. doi: 10.13205/j.hjgc.202205001
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

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

doi: 10.13205/j.hjgc.202403031
  • Received Date: 2023-02-28
    Available Online: 2024-05-31
  • 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.
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    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 11.5 %其他: 11.5 %其他: 1.0 %其他: 1.0 %China: 0.3 %China: 0.3 %United Kingdom: 1.6 %United Kingdom: 1.6 %东莞: 0.6 %东莞: 0.6 %临汾: 0.6 %临汾: 0.6 %保定: 0.3 %保定: 0.3 %北京: 4.8 %北京: 4.8 %十堰: 1.0 %十堰: 1.0 %南京: 1.0 %南京: 1.0 %南宁: 0.3 %南宁: 0.3 %南通: 0.6 %南通: 0.6 %台州: 3.2 %台州: 3.2 %嘉兴: 0.3 %嘉兴: 0.3 %天津: 2.2 %天津: 2.2 %宜昌: 0.3 %宜昌: 0.3 %宣城: 0.3 %宣城: 0.3 %常德: 0.3 %常德: 0.3 %广州: 0.3 %广州: 0.3 %张家口: 0.6 %张家口: 0.6 %成都: 0.3 %成都: 0.3 %扬州: 3.5 %扬州: 3.5 %拉贾斯坦邦: 0.3 %拉贾斯坦邦: 0.3 %昆明: 0.3 %昆明: 0.3 %晋城: 0.6 %晋城: 0.6 %朝阳: 0.3 %朝阳: 0.3 %杭州: 2.6 %杭州: 2.6 %武威: 0.6 %武威: 0.6 %武汉: 0.6 %武汉: 0.6 %济源: 0.6 %济源: 0.6 %温州: 1.0 %温州: 1.0 %湖州: 3.5 %湖州: 3.5 %漯河: 5.4 %漯河: 5.4 %石家庄: 0.6 %石家庄: 0.6 %芒廷维尤: 32.3 %芒廷维尤: 32.3 %芝加哥: 1.0 %芝加哥: 1.0 %苏州: 2.2 %苏州: 2.2 %衡水: 0.3 %衡水: 0.3 %衢州: 1.0 %衢州: 1.0 %西宁: 4.2 %西宁: 4.2 %西安: 0.3 %西安: 0.3 %贵阳: 0.3 %贵阳: 0.3 %运城: 2.6 %运城: 2.6 %遵义: 0.3 %遵义: 0.3 %邯郸: 1.0 %邯郸: 1.0 %郑州: 0.3 %郑州: 0.3 %重庆: 0.6 %重庆: 0.6 %长沙: 1.3 %长沙: 1.3 %长治: 0.6 %长治: 0.6 %其他其他ChinaUnited Kingdom东莞临汾保定北京十堰南京南宁南通台州嘉兴天津宜昌宣城常德广州张家口成都扬州拉贾斯坦邦昆明晋城朝阳杭州武威武汉济源温州湖州漯河石家庄芒廷维尤芝加哥苏州衡水衢州西宁西安贵阳运城遵义邯郸郑州重庆长沙长治

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      沈阳化工大学材料科学与工程学院 沈阳 110142

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