An Accuracy of Identifying Recyclable Objects and the Number of Objects Identified from Municipal Waste Without Occlusion Using Computer Vision Techniques

The proper disposal and recycling of waste products are critical concerns for municipalities worldwide. In recent years, the development of machine learning algorithms has led to automate the identification and separation of recyclable objects from non-recyclable objects. The effective management of municipal waste has become a critical challenge in modern urban environments. This study addresses the task of accurately identifying recyclable objects and determining their quantities within municipal waste streams, leveraging advanced computer vision techniques. The objective is to develop a robust system capable of detecting recyclable objects while overcoming the challenges posed by occlusions. The proposed approach combines state-of-the-art object detection algorithms with innovative occlusion handling methods to achieve accurate identification of recyclable items within complex waste compositions. This approach offers a promising avenue for enhancing waste management practices, providing actionable data for informed decision-making in urban sustainability.