CNN-Based Inspection Module for Liquid Carton Recycling by the Reverse Vending Machine
نویسندگان
چکیده
To protect our planet, the material recycling of domestic waste is necessary. Since COVID-19 pandemic began, volume has surged overwhelmingly, and many countries suffered from poor management. Increased demand for food delivery online shopping led to a huge surge in plastic paper which came natural resources. reduce consumption resources environment pollution, such as that landfills, should be recycled. One precious recyclable materials household liquid cartons are made high-quality paper. promote sustainable recycling, this proposes vision-based inspection module based on convolutional neural networks via transfer learning (CNN-TL) collecting packaging reverse vending machine (RVM). The RVM an unmanned automatic collector, thus it needs intelligence inspect whether deposited item acceptable or not. whole processing algorithm cartons, including step, presented. When inserted into by user after scanning barcode waste, relocated module, weighed. develop inspector, experimental set-up with video camera was built image data generation preparation. Using data, agent trained. make good selection model, 17 pretrained CNN models were evaluated, DenseNet121 selected. access performance cameras, four different types also evaluated. With same found effect number training epochs being set 10, 100, 500. In results, most accurate 500-epoch expected. By using process logic results showed accuracy detection over 99% (overall probability three inspections), time one less than 2 s. conclusion, proposed model verified would applicable RVM, could distinguish other waste.
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ژورنال
عنوان ژورنال: Sustainability
سال: 2022
ISSN: ['2071-1050']
DOI: https://doi.org/10.3390/su142214905