Journal article
Intelligent System Utilizing HOG and CNN for Thermal Image-Based Detection of Wild Animals in Nocturnal Periods for Vehicle Safety
Applied artificial intelligence, v 36(1), 2031825
31 Dec 2022
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Animal Vehicle Collision, commonly called roadkill, is an emerging threat to drivers and wild animals, increasing fatalities every year. Currently, prevalent methods using visible light cameras are efficient for animal detection in daylight time. This paper focuses on locating wildlife close to roads during nocturnal hours by utilizing thermographic obtained images, thus enhancing vehicle safety. In particular, it proposes an intelligent system for animal detection during nighttime that combines the technique of Histogram of Oriented Gradients (HOG) with a Convolutional Neural Network (CNN). The proposed intelligent system is benchmarked against a variety of CNN's like basic CNN and VGG16-based CNN and also with the machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), Decision Tree Algorithm (DT), Linear Regression (LR), and Gaussian Naive Bayes (GNB). The proposed detection system was tested on a set of real-world data acquired with a thermal camera on the move in the city of San Antonio, TX, USA that includes images of wild deer. Obtained results exhibit that the HOG-CNN combination achieved approximately 91% correct detection accuracy of wild deer on roadsides, while it outperformed the rest of the tested machine learning algorithms.
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Details
- Title
- Intelligent System Utilizing HOG and CNN for Thermal Image-Based Detection of Wild Animals in Nocturnal Periods for Vehicle Safety
- Creators
- Yuvaraj Munian - The University of Texas at San AntonioAntonio Martinez-Molina - Univ Texas San Antonio Utsa, Sch Architecture & Design, San Antonio, TX 78249 USADimitrios Miserlis - Univ Texas Hlth Sci Ctr, Dept Surg Vasc Surg, Houston, TX USAHermilo Hernandez - 25 2 Solut Corp, Pocatello, ID USAMiltiadis Alamaniotis - The University of Texas at San Antonio
- Publication Details
- Applied artificial intelligence, v 36(1), 2031825
- Publisher
- Taylor & Francis
- Number of pages
- 29
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Architecture, Design, and Urbanism
- Web of Science ID
- WOS:000753031000001
- Scopus ID
- 2-s2.0-85124886235
- Other Identifier
- 991021890005504721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic