An Approach to Automatic Garbage Detection Framework Designing using CNN

Abstract

This paper proposes a system for automatic detection of litter and garbage dumps in CCTV feeds with the help of deep learning implementations. The designed system named Greenlock scans and identifies entities that resemble an accumulation of garbage or a garbage dump in real time and alerts the respective authorities to deal with the issue by locating the point of origin. The entity is labelled as garbage if it passes a certain similarity threshold. ResNet-50 has been used for the training purpose alongside TensorFlow for mathematical operations for the neural network. Combined with a pre-existing CCTV surveillance system, this system has the capability to hugely minimize garbage management costs via the prevention of formation of big dumps. The automatic detection also saves the manpower required in manual surveillance and contributes towards healthy neighborhoods and cleaner cities. This article is also showing the comparison between applied various algorithms such as standard TensorFlow, inception algo and faster-r CNN and Resnet-50, and it has been observed that Resnet-50 performed with better accuracy. The study performed here proved to be a stress reliever in terms of the garbage identification and dumping for any country. At the end of the article the comparison chart has been shown.

Solid Waste Detection and Recognition using Faster RCNN

“…These filters begin by recognizing simple features like brightness and edges, and progressively advance to more complex features that uniquely define objects. The general CNN architecture is depicted in Figure 3, and for a more detailed understanding of CNN, refer to (11,12) . Deep neural networks face a bottleneck where their performance declines as they become deeper.…”