Real-Time Trash Detection for Modern Societies using CCTV to Identifying Trash by utilizing Deep Convolutional Neural Network

TL;DR

This paper presents a real-time trash detection system using CNNs and CCTV cameras, aiming to improve environmental cleanliness in modern societies by identifying and recording individuals who litter.

Contribution

The paper introduces a custom dataset and a CNN-based model for real-time trash detection, demonstrating superior accuracy and mAP performance over previous methods.

Findings

The CNN model achieved higher accuracy and mAP in trash detection.

The system successfully detects and records littering behavior in real-time.

A new complex dataset with over 2100 images was created for training.

Abstract

To protect the environment from trash pollution, especially in societies, and to take strict action against the red-handed people who throws the trash. As modern societies are developing and these societies need a modern solution to make the environment clean. Artificial intelligence (AI) evolution, especially in Deep Learning, gives an excellent opportunity to develop real-time trash detection using CCTV cameras. The inclusion of this project is real-time trash detection using a deep model of Convolutional Neural Network (CNN). It is used to obtain eight classes mask, tissue papers, shoppers, boxes, automobile parts, pampers, bottles, and juices boxes. After detecting the trash, the camera records the video of that person for ten seconds who throw trash in society. The challenging part of this paper is preparing a complex custom dataset that took too much time. The dataset consists of more than 2100 images. The CNN model was created, labeled, and trained. The detection time accuracy and average mean precision (mAP) benchmark both models' performance. In experimental phase the mAP performance and accuracy of the improved CNN model was superior in all aspects. The model is used on a CCTV camera to detect trash in real-time.