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Cognitive cloud framework for waste dumping analysis using deep learning vision computing in healthy environment
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DOI：10.1016/j.compeleceng.2023.108814.png)
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Increased population and usage of more unnatural particles create dangerous waste in society. Environmental Sustainability is ensured based on proper waste management solutions. There is a need for real-time monitoring of waste dumping in the respective area to alert the individual management team to control it. We specifically investigated a solution to the issue of preventing the illegal disposal of waste in smart cities. In this regard, we will discuss the working model of an automatic visual identification and warning system. The proposed method uses cognitive computing technologies to analyze films taken by cameras located in metropolitan areas. To locate huge trash waste where it shouldn't be and alert the municipality to the situation. Initially, the proposed method develops the application prototype. Next, the Deep learning method Single Shot MultiBox Detector (SSD) MobileNet v2 is used to detect bulky waste data. Finally, the CNN method classifies the data and triggers the alarm system. The alarm system alerts the municipality with warning sounds to remove waste dumping. The proposed method provides an intelligent waste management system and a healthy environment. The simulation outcomes demonstrate that the suggested strategy yields a superior outcome.
Keyword:
Waste management
Video extensive data analysis
Cloud computing
Cognitive computing
Sustainable environment
Deep learning
MobileNet v2
Convolutional neural network
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