ICITR - 2023

Permanent URI for this collectionhttp://192.248.9.226/handle/123/22075

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Browsing ICITR - 2023 by Subject "Anomaly detection"

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    Early identification of deforestation using anomaly detection
    (Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Wijesinghe, N; Perera, R; Sellahewa, N; Talagala, PD; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
    Research involving anomaly detection in image streams has seen growth through the years, given the proliferation of high-quality image data in various applications. One such application that is in urgent need of attention is deforestation. Detecting anomalies in this context, however, remains challenging due to the irregular and low-probability nature of deforestation events. This study introduces two anomaly detection frameworks utilizing machine learning and deep learning for the early detection of deforestation activities in image streams. Furthermore, Explainable AI was used to explain the black box models of the deep learning-based anomaly detection framework. The class imbalance problem, the inter-dependency between the images with time, the lack of available labelled images, a datadriven anomalous threshold, and the trade-off of accuracy while increasing interpretability in the black box optimization methods are some key aspects considered in the model-building process. Our novel framework for anomaly detection in image streams underwent rigorous evaluation using a range of datasets that included synthetic and real-world data, notably datasets related to Amazon’s forest coverage. The objective of this evaluation was to detect occurrences of deforestation in the Amazon. Several metrics were used to evaluate the performance of the proposed framework.
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    Using multispectral uav imagery for marine debris detection in Sri Lanka
    (Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Velayuthan, P; Piyathilake, V; Athapaththu, K; Sandaruwan, D; Sayakkara, AP; Hettiarachchi, H; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
    Marine pollution is a significant issue in Sri Lanka, with the country being a major contributor to marine debris. Marine pollution has the potential to adversely impact marine and coastal biodiversity, as well as the fishing and tourism industries. Current methods for monitoring marine debris involve labor-intensive approaches, such as visual surveys conducted from boats or aircraft, beach clean-ups, and underwater transects by divers. However, an emerging trend in many countries is the use of Unmanned Aerial Vehicle (UAV) imagery for monitoring marine debris due to its advantages, including reduced labour requirements, higher spatial resolution, and cost-effectiveness. The work presented in this study utilizes multispectral UAV imagery to monitor marine debris in a coastal area of Ambalangoda, Sri Lanka. For the automated detection of marine debris in captured images, this work replicates the state-of-the-art CutPaste method for region detection and utilized the ResNet-18 model with Faster R-CNN for the final classification of marine debris instances. The implemented approach demonstrated a classification accuracy of approximately 60% in automatic marine debris detection, laying the groundwork for potential enhancements in the future.