Integrating HOG-Based Vehicle Detection with CNN-Based Lane Detection for Autonomous Driving
The advancement of autonomous driving systems hinges on accurate and reliable vehicle and lane detection. This paper presents an integrated method to improve autonomous driving systems by merging Histogram of Oriented Gradients (HOG)-based vehicle detection with Convolutional Neural Network (CNN)...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2024
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/1983/ http://eprints.intimal.edu.my/1983/2/joit2024_09b.pdf http://eprints.intimal.edu.my/1983/3/522 |
| _version_ | 1848766888702640128 |
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| author | R., Karthickmanoj S.Aasha, Nandhini D., Lakshmi R., Rajasree |
| author_facet | R., Karthickmanoj S.Aasha, Nandhini D., Lakshmi R., Rajasree |
| author_sort | R., Karthickmanoj |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | The advancement of autonomous driving systems hinges on accurate and reliable vehicle and lane
detection. This paper presents an integrated method to improve autonomous driving systems by
merging Histogram of Oriented Gradients (HOG)-based vehicle detection with Convolutional
Neural Network (CNN)-based lane detection. HOG effectively identifies vehicles by capturing
edge orientations and structural features, while CNNs excel in detecting intricate lane patterns
through deep learning. The combination of these techniques offers a robust solution for detecting
both vehicles and lanes, essential for autonomous navigation. Evaluated across a diverse dataset
featuring various driving conditions, the system's performance is measured using precision, recall,
F1 score (for vehicle detection), and accuracy (for lane detection). The results indicate significant
enhancements in detection capabilities, leading to improved situational awareness and safer
navigation. Future work will aim to refine the system further and tackle challenges in more
complex driving environments, marking this approach as a promising advancement in autonomous
driving technology. |
| first_indexed | 2025-11-14T11:58:18Z |
| format | Article |
| id | intimal-1983 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:58:18Z |
| publishDate | 2024 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-19832025-06-13T03:26:34Z http://eprints.intimal.edu.my/1983/ Integrating HOG-Based Vehicle Detection with CNN-Based Lane Detection for Autonomous Driving R., Karthickmanoj S.Aasha, Nandhini D., Lakshmi R., Rajasree T Technology (General) TA Engineering (General). Civil engineering (General) TL Motor vehicles. Aeronautics. Astronautics The advancement of autonomous driving systems hinges on accurate and reliable vehicle and lane detection. This paper presents an integrated method to improve autonomous driving systems by merging Histogram of Oriented Gradients (HOG)-based vehicle detection with Convolutional Neural Network (CNN)-based lane detection. HOG effectively identifies vehicles by capturing edge orientations and structural features, while CNNs excel in detecting intricate lane patterns through deep learning. The combination of these techniques offers a robust solution for detecting both vehicles and lanes, essential for autonomous navigation. Evaluated across a diverse dataset featuring various driving conditions, the system's performance is measured using precision, recall, F1 score (for vehicle detection), and accuracy (for lane detection). The results indicate significant enhancements in detection capabilities, leading to improved situational awareness and safer navigation. Future work will aim to refine the system further and tackle challenges in more complex driving environments, marking this approach as a promising advancement in autonomous driving technology. INTI International University 2024-08 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/1983/2/joit2024_09b.pdf text en cc_by_4 http://eprints.intimal.edu.my/1983/3/522 R., Karthickmanoj and S.Aasha, Nandhini and D., Lakshmi and R., Rajasree (2024) Integrating HOG-Based Vehicle Detection with CNN-Based Lane Detection for Autonomous Driving. Journal of Innovation and Technology, 2024 (09). pp. 1-6. ISSN 2805-5179 http://ipublishing.intimal.edu.my/joint.html |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TL Motor vehicles. Aeronautics. Astronautics R., Karthickmanoj S.Aasha, Nandhini D., Lakshmi R., Rajasree Integrating HOG-Based Vehicle Detection with CNN-Based Lane Detection for Autonomous Driving |
| title | Integrating HOG-Based Vehicle Detection with CNN-Based Lane Detection
for Autonomous Driving |
| title_full | Integrating HOG-Based Vehicle Detection with CNN-Based Lane Detection
for Autonomous Driving |
| title_fullStr | Integrating HOG-Based Vehicle Detection with CNN-Based Lane Detection
for Autonomous Driving |
| title_full_unstemmed | Integrating HOG-Based Vehicle Detection with CNN-Based Lane Detection
for Autonomous Driving |
| title_short | Integrating HOG-Based Vehicle Detection with CNN-Based Lane Detection
for Autonomous Driving |
| title_sort | integrating hog-based vehicle detection with cnn-based lane detection
for autonomous driving |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TL Motor vehicles. Aeronautics. Astronautics |
| url | http://eprints.intimal.edu.my/1983/ http://eprints.intimal.edu.my/1983/ http://eprints.intimal.edu.my/1983/2/joit2024_09b.pdf http://eprints.intimal.edu.my/1983/3/522 |