Abstract
The progressive contamination of lands, oceans, and rivers by garbage is a growing concern. The pollution imposed by garbage accumulation can lead to serious health hazards as well as economic and environmental repercussions; this accumulation derives from irresponsible waste disposal. To counter the surplus of garbage pollution, this paper proposes an accurate, low-energy consuming system for a trash allocation robot, namely, Wall-E. Wall-E is a Waste Allocation Load Lifter Model for trash detection based on deep learning and wireless localization techniques. It is based on the integration of the deep learning models for trash detection and classification as well as a navigation system that is more accurate and energy-efficient than the commonly used global position system (GPS). The paper tests and analyses the neural network algorithms YOLOv7 and YOLOv8 conducted on a trash dataset and concludes that YOLOv7 is of higher performance in trash detection and classification. To implement this algorithm on a real-time robotic system, image frames will be sent from a Pi camera at a high frequency to a Raspberry Pi board that has YOLOv7 installed. In order to conceptually determine a suitable navigation system for our robot, the paper discusses the common challenges faced in traditional navigation systems, including proposed systems in related works and their limitations to our proposed system. In our conducted experiments, the proposed Wall-E achieved the highest accuracy in object detection using YOLOv7, which will be utilized in our robot to detect trash items sent through image frames from a Pi camera. The Dejavu navigation system was concluded as the most appropriate for our system’s requirements.
S. Elghamrawy–Senior IEEE Member.
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Mohsen, L., Talaat, A., Elghamrawy, S. (2023). Waste Allocation Load Lifter Model for Trash Detection Based on Deep Learning and Wireless Localization Techniques. In: Hassanien, A., Rizk, R.Y., Pamucar, D., Darwish, A., Chang, KC. (eds) Proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics 2023. AISI 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 184. Springer, Cham. https://doi.org/10.1007/978-3-031-43247-7_5
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