A Simple and fast method to detect garbage dumping using pedestrian attribute

Kang, Mi-Seon; Kim, Pyong-Kun; Lim, Kil-Taek

doi:10.1109/icce-asia49877.2020.9276940

Cited by 3 publications

(1 citation statement)

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“…In studies where ultrasonic sensors are used, a web camera placed on the robot detects the garbage in the images. Kang et al (Kang, Kim, & Lim, 2020) used the video image data set created using CCTV cameras in order to detect the garbage existing in the environment where objects and people are located, and classified them with K-Nearest algorithms among machine learning algorithms. The study presents a new technique for the detection of garbage in crowded environments.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Garbage Detection for Autonomous Garbage Collection Vehicles

Uzun¹,

KARACA²

2022

European Journal of Science and Technology

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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.

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Section: Introductionmentioning

confidence: 99%