Revolutionizing Waste Management Through the Integration of IoT and Deep Learning Technology

Abstract

The constantly growing volume of waste produced daily by people and businesses has made the management of waste in urban contexts a challenging issue. These days, it's common practice to combine deep learning and the internet of things model with efficient waste management tactics to offer flexible real-time data monitoring and categorization solutions. The proposed methodology involved IoT-enabled garbage management which is used to detect the level of thrash and to gather images of the garbage objects from the onboard camera. In this model, the bounding-box regressor and the object classifier are removed. Instead, the garbage detection and segregation process uses the backbone model architecture of TensorFlow on ResNet. It is trained to detect and recognize different types of waste items in images. The trained deep learning model is integrated into the trash bin to categorize waste products into relevant groups (such as biodegradable and non-biodegradable). The method outperforms other existing methods in terms of garbage segmentation and categorization.