Seamless monitoring and detection of waste hazards in floating water and water reservoirs using Internet of Things integrated deep learning algorithms
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One of the most essential and current research areas is waste hazard monitoring in floating water and water reservoirs. The increasing population and urbanisation contribute to waste hazards, negatively impacting water quality, human health, and environmental resources. Many research methods have focused on applying metaheuristic and image-processing techniques to analyse and detect waste hazards in floating water. The detection efficiency was good; however, their computational complexity was high and not cost-effective. Additionally, it takes longer to analyse the data. Coastal, riverine, and seaside areas require effective detection and monitoring systems to generate alerts for waste-hazard removal. Otherwise, these hazards pose numerous health risks to the public and degrade water quality. However, it remains a complex technological challenge due to real-time constraints, environmental changes, and the lack of automation in traditional systems. This paper addresses this major challenge and aims to design and implement an IoT-integrated deep learning model incorporating principal component analysis (PCA), grey-level co-occurrence matrix (GLCM), and Fast R-CNN to enable automatic, optimal waste-hazard detection in dynamic floating and static water bodies. Various IoT sensors and edge devices are installed in water bodies to collect data. Initially, the PCA method analyses the data and improves the entire Fast R-CNN model by efficiently extracting, compressing, and denoising features, while GLCM captures discriminative textural information. Moreover, the Fast R-CNN model reduces computational complexity while improving detection and classification accuracy. Both input and predicted data are securely transmitted through fog computing and interconnected throughout the entire architecture. The deep learning model is implemented with IoT data, and the results are validated. The output demonstrates that PCA-GLCM-integrated Fast R-CNN provides high accuracy in detecting different types of waste hazards with a lower false-positive rate and reduced latency.
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