Abstract
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.
Highlights
The current state of tracking waste containers in municipalities is rigid, inefficient, and hard to oversee [1]
We propose a tracking system for waste containers based on computer vision
Content-based image retrieval (CBIR) [10,11] involves the retrieval of images based on the representation of visual content to identify images that are relevant to the query
Summary
The current state of tracking waste containers in municipalities is rigid, inefficient, and hard to oversee [1]. We propose a tracking system for waste containers based on computer vision This solution would remove the constraints and disadvantages of RFID, since it does not require direct interaction with containers or a manual installation of any sort, and, deal with the problems of flexibility and cost and the environmental impact. This is an opportunity to experiment with computer vision on tasks that involve the use of complex technology.
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