Empowering the next generation leaders of empowering http://repo.lib.sab.ac.lk:8080/xmlui/handle/susl/3772 2026-04-19T22:49:08Z 2026-04-19T22:49:08Z Applied Sciences, Faculty of http://repo.lib.sab.ac.lk:8080/xmlui/handle/susl/3864 2023-09-14T07:28:43Z 2023-05-31T00:00:00Z

Cover page Applied Sciences, Faculty of

2023-05-31T00:00:00Z Applied Sciences, Faculty of http://repo.lib.sab.ac.lk:8080/xmlui/handle/susl/3863 2023-09-14T07:26:54Z 2023-05-31T00:00:00Z

Contents Applied Sciences, Faculty of

2023-05-31T00:00:00Z Applied Sciences, Faculty of http://repo.lib.sab.ac.lk:8080/xmlui/handle/susl/3862 2023-09-14T07:23:18Z 2023-05-31T00:00:00Z

Front Page Applied Sciences, Faculty of

2023-05-31T00:00:00Z Chathuranga, W.A.D. Malkanthi, A.M.C. http://repo.lib.sab.ac.lk:8080/xmlui/handle/susl/3861 2023-09-14T06:38:32Z 2023-05-31T00:00:00Z

Comparison of Animal-Vehicle Collision Avoidance Systems Using Image Processing Technique Chathuranga, W.A.D.; Malkanthi, A.M.C. Animal-vehicle collision (AVC) is a significant global issue that causes considerable loss of life and property damage. Real-time animal detection using computer vision techniques and machine learning algorithms is considered to be the most effective way to prevent AVC. This study presents a review of the state of art methods used for animal detection and prevention of AVC using image processing techniques. Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), and Haar features are the commonly used methods for object and animal detection. However, challenges such as image scale and viewpoint variability, background clutter, lighting conditions, image quality, and occlusion lower the accuracy and effectiveness of these methods. Various classifiers such as K-way logistic regression, support vector machine (SVM), and K-nearest neighbors (KNN) are used to overcome these problems and evaluate features. LBP-AdaBoost and HOG features showed better results than others, with a detection rate of 91%, when extracted from regions of interest (ROIs). The performance of the system is further improved when combined with HOG-SVM. Thus, HOG and LBP features can be useful for detecting animals despite the defects in images. The best method for AVC detection is HOG-SVM with a detection accuracy rate of 92%. DCNN, HOG-AdaBoost, and LBP-AdaBoost showed an accuracy rate of 91%, 84%, and 82% respectively. The worst performance was seen when the Haar-AdaBoost method was used (79%). However, the effectiveness of each technique depends mainly on the accuracy of real-time animal detection, the signal transmission speed from the system to the driver, and the vehicle speed. Comparative analysis can be carried out considering animal detection for methods used in foggy weather conditions and the effect of speed variations of the vehicle on the results. Expanding the scope of detection to cover a broader range of animal species is proposed as a future direction of AVC systems.

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