Abstract
In this paper, an image-based waste collection scheduling involving a node with three waste bins is considered. First, the system locates the three bins and determines the waste level of each bin using four Laws Masks and a set of Support Vector Machine (SVM) classifiers. Next, a Hidden Markov Model (HMM) is used to decide on the number of days remaining before waste is collected from the node. This decision is based on the HMM’s previous state and current observations. The HMM waste collection scheduling seeks to maximize the number of days between collection visits while preventing waste contamination due to late collection. The proposed system was trained using 100 training images and then tested on 100 test images. Each test image contains three bins that might be shifted, rotated, occluded or toppled over. The upright bins could be empty, partially full or full of garbage of various shapes and sizes. The method achieves bin detection, waste level classification and collection day scheduling rates of 100%, 99.8% and 100% respectively.
Highlights
An effective Solid Waste Management (SWM) system is crucial to manage increasing solid waste generated by a growing population and maintain cleanliness of the waste sites [1]
Solid waste collection scheduling is an integral part of a waste management model
Only one empty bin was misclassified as partially full by Support Vector Machine (SVM) classifier and this amounts to a classification rate of 99.73%
Summary
An effective Solid Waste Management (SWM) system is crucial to manage increasing solid waste generated by a growing population and maintain cleanliness of the waste sites [1]. A proper SWM is expensive as it requires funding, personnel, equipment, infrastructure and an efficient operation strategy. An integrated waste management system for an urban population requires careful planning of the movement of waste materials from the generation points to the treatment and disposal sites [3]. Solid waste collection scheduling is an integral part of a waste management model. It is generally regarded as a form of vehicle routing problem (VRP) which seeks to minimize the trucks used and the distance traversed.
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