Research Publications Authored by SLIIT Staff

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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.

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Author: search.filters.author.Kasthurirathna, DSubject: search.filters.subject.Computer Vision

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    Smart Plant Disorder Identification using Computer Vision Technology
    (IEEE, 2020-11-04) Manoharan, S; Sariffodeen, B; Ramasinghe, K. T; Rajaratne, L. H; Kasthurirathna, D; Wijekoon, J
    The soil composition around the world is depleting at a rapid rate due to overexploitation by the unsustainable use of fertilizers. Streamlining the availability of nutrient deficiency and fertilizer related knowledge among impoverished farming communities would promoter environmentally and scientifically sustainable farming practices. Thus, contributing to several Sustainable Development Goals set out by the United Nations. The most direct solution to the inappropriate fertilizer usage is to add only the necessary amounts of fertilizer required by plants to produce a significant yield without nutrition deficiencies. To this end this paper proposes a Smart Nutrient Disorder Identification system employing computer vision and machine learning techniques for identification purposes and a decentralized blockchain platform to streamline a bias-less procurement system. The proposed system yielded 88% accuracy in disorder identification, while also enabling secure, transparent flow of verified information.
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    Computer Vision and NLP based Multimodal Ensemble Attentiveness Detection API for E-Learning
    (IEEE, 2021-04-21) Wijeratne, M. D; Lakmal, R. H. G. A; Geethadhari, W. K. S; Athalage, M. A; Gamage, A; Kasthurirathna, D
    Attention is the fundamental element of effective learning, memory, and interaction. Learning however, with the evolvement of technologies in the modern digital age, has surpassed traditional learning systems to more convenient online or e-learning systems. Nevertheless, unlike in the traditional learning systems, attention detection of a student in an e-learning environment remains one of the barely explored areas in Human Computer Interaction. This study proposes a multimodal ensemble solution to detect the level of attentiveness of a student in an e-learning environment, with the use of computer vision, natural language processing, and deep learning to overcome the barriers in identifying user attention in e-learning. The proposed multimodal captures, processes, and predicts user attentiveness levels of individual students, which are subsequently aggregated through an ensemble model to derive an overall outcome of better accuracy than individual model outcomes. The final outcome of the ensemble model produces a range of percentages, within which the attentiveness level of the student lies during a single online lesson. This range is consequently delivered to the users through an Application Programming Interface.
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    Computer Vision Enabled Drowning Detection System
    (IEEE, 2021-12-09) Handalage, U; Nikapotha, N; Subasinghe, C; Prasanga, T; Thilakarthna, T; Kasthurirathna, D
    Safety is paramount in all swimming pools. The current systems expected to address the problem of ensuring safety at swimming pools have significant problems due to their technical aspects, such as underwater cameras and methodological aspects such as the need for human intervention in the rescue mission. The use of an automated visual-based monitoring system can help to reduce drownings and assure pool safety effectively. This study introduces a revolutionary technology that identifies drowning victims in a minimum amount of time and dispatches an automated drone to save them. Using convolutional neural network (CNN) models, it can detect a drowning person in three stages. Whenever such a situation like this is detected, the inflatable tube-mounted self-driven drone will go on a rescue mission, sounding an alarm to inform the nearby lifeguards. The system also keeps an eye out for potentially dangerous actions that could result in drowning. This system’s ability to save a drowning victim in under a minute has been demonstrated in prototype experiments' performance evaluations.
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    Computer Vision for Autonomous Driving
    (IEEE, 2021-12-09) Kanchana, B; Peiris, R; Perera, D; Jayasinghe, D; Kasthurirathna, D
    Computer vision in self-driving vehicles can lead to research and development of futuristic vehicles that can mitigate the road accidents and assist in a safer driving environment. By using the self-driving technology, the riders can be roamed to their destinations without using human interaction. But in recent times self-driving vehicle technology is still at the early stage. Mostly in the rushed areas like cities it becomes challenging to deploy such autonomous systems because even a small amount of data can cause a critical accident situation. In Order to increase the autonomous driving conditions computer vision and deep learning-based approaches are tended to be used. Finding the obstacles on the road and analyzing the current traffic flow are mainly focused areas using computer vision-based approaches. As well as many researchers using deep learning-based approaches like convolutional neural networks to enhance the autonomous driving conditions. This research paper focused on the evaluation of computer vision used in self-driving vehicles.
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    Computer vision based indoor navigation for shopping complexes
    (acm.org, 2020-12-09) Perera, G. S. T; Madhubhashini, K. W. R; Lunugalage, D; Piyathilaka, D. V. S; Lakshani, W. H. U; Kasthurirathna, D
    Smartphone-based indoor navigation systems are frantically required in indoor situations. This limitation of clients is significant. Global Positioning System (GPS) isn't plausible for indoor areas as it gives exceptionally helpless outcomes for indoor restriction. In this research paper, we present a Computer Vision-Based Indoor Navigation System for shopping complexes. Computer vision is used in this system to find the exact location/current location of the user. It contains a mobile android application for positioning, navigating, and displaying the current location for showing on 2D Map. The system will detect the user's position, generate a GIS map, display the shortest path using A* search algorithm, and provides step-by-step direction to the destination using audio instruction for localization with Augmented Reality (AR) map and navigation using mobile phone sensor technologies like accelerometer, gyroscope, and magnetometer. The audio instructions include active guidance for upcoming turns in the traveling path, distance of each section between turns. This system uses a suggestion-based Chabot that uses a trained model to improve the user's experience. Thus, this research expects to build a cost-effective, efficient, and timely response system that will help the users for a smart shopping experience.