Deep Learning Based Garbage Detection for Autonomous Garbage Collection Vehicles

Abstract

With the rapid technological advances and the increasing human population, the need for more production has emerged and consumption has increased accordingly. As a result of this increased consumption, more garbage has been generated. Environmental pollution caused by these garbages emerges as a problem that people have to overcome both in Turkey and in the world. Many studies have been conducted to overcome this problem. Especially today, with the development of autonomous vehicles and artificial intelligence, the solutions using these technologies have increased. In this study, a new data set was created for autonomous garbage collection vehicles and a model was proposed in which these vehicles can be used. The data set was prepared with images of garbage with paper cups, which is one of the most polluting garbage, taken in different places, and images consisting of different garbage without paper cups. Paper cups were detected from these images with pre-trained Squenzenet, VGG-19 and GoogLeNet convolutional neural networks. The performance rate of the SquenzeNet, GoogLeNet and Vgg-19 networks used in the study was found as 97.77%, 96.44% 94.66%, respectively.

Detecting Litter in Street Sweepers Using Deep Learning

“…In the present day, there are various methods for waste detection, and studies related to waste detection using YOLOv7 have also been conducted. Uzun and Karaca (2022) conducted a study on deep learning-based waste detection for autonomous waste collection vehicles. In this study, 500 images of paper cups and 250 images of other types of waste were classified using deep learning algorithms, and a new dataset was proposed.…”

“…Training is conducted to teach the AI system to recognize garbage and adjust the vacuum suction, making the system autonomous. This not only leads to energy savings but also prevents the vacuum from operating at full power, reducing energy waste and noise pollution (Uzun & Karaca, 2022).…”