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Detection of Waste Containers Using Computer Vision

Escola Superior de Tecnologia, Instituto Politécnico de Castelo Branco, 6000-767 Castelo Branco, Portugal
EVOX Technologies, 6000-767 Castelo Branco, Portugal
Instituto de Telecomunicações, Universidade da Beira Interior, 6201-001 Covilhã, Portugal
Departamento de Engenharia Eletromecânica, Universidade da Beira Interior, 6201-001 Covilhã, Portugal
Author to whom correspondence should be addressed.
Appl. Syst. Innov. 2019, 2(1), 11;
Received: 14 February 2019 / Revised: 11 March 2019 / Accepted: 19 March 2019 / Published: 20 March 2019
PDF [5306 KB, uploaded 25 March 2019]


This work is a part of an ongoing study to substitute the identification of waste containers via radio-frequency identification. The purpose of this paper is to propose a method of identification based on computer vision that performs detection using images, video, or real-time video capture to identify different types of waste containers. Compared to the current method of identification, this approach is more agile and does not require as many resources. Two approaches are employed, one using feature detectors/descriptors and other using convolutional neural networks. The former used a vector of locally aggregated descriptors (VLAD); however, it failed to accomplish what was desired. The latter used you only look once (YOLO), a convolutional neural network, and reached an accuracy in the range of 90%, meaning that it correctly identified and classified 90% of the pictures used on the test set. View Full-Text
Keywords: waste container; object detection; VLAD; convolutional neural networks; YOLO waste container; object detection; VLAD; convolutional neural networks; YOLO

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Valente, M.; Silva, H.; Caldeira, J.M.L.P.; Soares, V.N.G.J.; Gaspar, P.D. Detection of Waste Containers Using Computer Vision. Appl. Syst. Innov. 2019, 2, 11.

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