Garbage Detection System Using Image Processing

This research presents a brand new development system for garbage detection using advanced techniques in image processing. This system is primarily to identify and then classify the waste in real time, thus tackling waste management problems. This has been done using Convolutional Neural Networks (CNNs) and image preprocessing to achieve high accuracy in the detection of different waste types such as plastics, metals, and organics. The experimental results prove the model's robustness across diverse environments that make it amenable to large-scale deployment. The work is meant to serve municipalities, industries, and environmental organizations for automating the waste segregation process [1], [2]. The paper deliberates about some image processing techniques on edge detection, segmentation, and feature extraction since these form the basis for waste identification and classification types that rely on them. Integration of such methods into machine learning and deep learning algorithms, especially using convolutional neural networks (CNN), makes the process more accurate and robust in detection. Also discussed in the review are the pre-processing methods, such as noise reduction and contrast enhancement, which improve the quality of input data to achieve better detection results.