Research Publications
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Item Embargo A Lightweight YOLOv8n-Based Binary Waste Classification Model for Autonomous Garbage-Collecting Robots(Institute of Electrical and Electronics Engineers Inc., 2026-07-02) Perera W.B.N.; Sendanayaka H.K; Nuwanthi B.D.T.; Jayasekara R.G.S; Lokuliyana, S; Silva, SEfficient waste classification is critical for automation in modern waste management systems, especially in places where it is hard to use manual classification. In this work, a model for waste and non-waste classification based on the YOLOv8n framework, known for high efficiency and applicability for embedded systems, is designed. For this purpose, a customized dataset was collected using real photos under different lighting conditions, different backgrounds, and with varying objects to reflect a more realistic environment. The training process included augmentation, normalization, and hyperparameters tuning to improve the efficiency of the model. Evaluation metrics such as accuracy, precision, recall, F1 score, and confusion matrix were used. According to the experimental results, the developed model provides an accuracy of 92.5% and F1 score of 0.94 with maintaining fast inference performance for real-time applications. The designed framework aims to become a module in an autonomous waste collecting robot to validate objects before manipulating them by robots, ensuring reliable and efficient real-world system performance.Publication Open Access Smart Train-Elephant Collision Management System(Faculty of Engineering, 2025-09-09) Perera G.P.K.N; Nimsara P.P.; Perera P.R.D.N.; Weerasinghe T.G.J.N; Kularathna P.H.G.U.; Morapitiya S.SThis paper presents a novel solution for the Train-elephant collision issue. It is a national-level issue, and a higher number of elephants die yearly. Approximately 47 elephants died from 2021 to 2024 due to a trainelephant collision. Therefore, we introduce a novel technical management system to avoid train-elephant collisions. The primary objective of this work is to explore the details of the issue and implement the system to repel the elephant using a real-time warning system. Simulation and hardware implementation were both carried out for the final outputs. In addition, introduce a communication system to make the train driver and the two nearby stations aware. The study demonstrates the potential real-time implementation system for the train-elephant national-level issue.