Efficient Detection and Recognition of Traffic Lights for Autonomous Vehicles Using CNN
Smart city infrastructure and Intelligent Transportation Systems (ITS) need modern traffic monitoring and driver assistance systems such as autonomous traffic signal detection. ITS is a dominant research area among several fields in the domain of artificial intelligence. Traffic signal detection...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sukkur IBA University
2023-02-01
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| Series: | Sukkur IBA Journal of Emerging Technologies |
| Subjects: | |
| Online Access: | http://journal.iba-suk.edu.pk:8089/sibajournals/index.php/sjet/article/view/1181 |
| _version_ | 1797924715079139328 |
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| author | Tayyaba Sahar Hayl Khadami Muhammad Rauf |
| author_facet | Tayyaba Sahar Hayl Khadami Muhammad Rauf |
| author_sort | Tayyaba Sahar |
| collection | DOAJ |
| description |
Smart city infrastructure and Intelligent Transportation Systems (ITS) need modern traffic
monitoring and driver assistance systems such as autonomous traffic signal detection. ITS is a
dominant research area among several fields in the domain of artificial intelligence. Traffic
signal detection is a key module of autonomous vehicles where accuracy and inference time are
amongst the most significant parameters. In this regard, the aim of this study is to detect traffic
signals focusing to enhance accuracy and real-time performance. The results and discussion
enclose a comparative performance of a CNN-based algorithm YOLO V3 and a handcrafted
technique that gives insight for enhanced detection and inference in day and night light. It is
important to consider that real-world objects are associated with complex backgrounds,
occlusion, climate conditions, and light exposure that deteriorate the performance of sensitive
intelligent applications. This study provides a direction to propose a hybrid technique for TLD
not only in the daytime but also in night light.
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| first_indexed | 2024-04-10T15:05:28Z |
| format | Article |
| id | doaj.art-83a0f6abb6b44d5aa5941499999ea46c |
| institution | Directory Open Access Journal |
| issn | 2616-7069 2617-3115 |
| language | English |
| last_indexed | 2024-04-10T15:05:28Z |
| publishDate | 2023-02-01 |
| publisher | Sukkur IBA University |
| record_format | Article |
| series | Sukkur IBA Journal of Emerging Technologies |
| spelling | doaj.art-83a0f6abb6b44d5aa5941499999ea46c2023-02-15T05:04:34ZengSukkur IBA UniversitySukkur IBA Journal of Emerging Technologies2616-70692617-31152023-02-015210.30537/sjet.v5i2.1181Efficient Detection and Recognition of Traffic Lights for Autonomous Vehicles Using CNNTayyaba Sahar0Hayl KhadamiMuhammad RaufElectronic Engineering Department Dawood University of Engineering & Technology Smart city infrastructure and Intelligent Transportation Systems (ITS) need modern traffic monitoring and driver assistance systems such as autonomous traffic signal detection. ITS is a dominant research area among several fields in the domain of artificial intelligence. Traffic signal detection is a key module of autonomous vehicles where accuracy and inference time are amongst the most significant parameters. In this regard, the aim of this study is to detect traffic signals focusing to enhance accuracy and real-time performance. The results and discussion enclose a comparative performance of a CNN-based algorithm YOLO V3 and a handcrafted technique that gives insight for enhanced detection and inference in day and night light. It is important to consider that real-world objects are associated with complex backgrounds, occlusion, climate conditions, and light exposure that deteriorate the performance of sensitive intelligent applications. This study provides a direction to propose a hybrid technique for TLD not only in the daytime but also in night light. http://journal.iba-suk.edu.pk:8089/sibajournals/index.php/sjet/article/view/1181Object Detection; Convolutional Neural Networks; You Only Look Once; Intelligent Transportation Systems; Hough Transform; Traffic Signal Lights. |
| spellingShingle | Tayyaba Sahar Hayl Khadami Muhammad Rauf Efficient Detection and Recognition of Traffic Lights for Autonomous Vehicles Using CNN Sukkur IBA Journal of Emerging Technologies Object Detection; Convolutional Neural Networks; You Only Look Once; Intelligent Transportation Systems; Hough Transform; Traffic Signal Lights. |
| title | Efficient Detection and Recognition of Traffic Lights for Autonomous Vehicles Using CNN |
| title_full | Efficient Detection and Recognition of Traffic Lights for Autonomous Vehicles Using CNN |
| title_fullStr | Efficient Detection and Recognition of Traffic Lights for Autonomous Vehicles Using CNN |
| title_full_unstemmed | Efficient Detection and Recognition of Traffic Lights for Autonomous Vehicles Using CNN |
| title_short | Efficient Detection and Recognition of Traffic Lights for Autonomous Vehicles Using CNN |
| title_sort | efficient detection and recognition of traffic lights for autonomous vehicles using cnn |
| topic | Object Detection; Convolutional Neural Networks; You Only Look Once; Intelligent Transportation Systems; Hough Transform; Traffic Signal Lights. |
| url | http://journal.iba-suk.edu.pk:8089/sibajournals/index.php/sjet/article/view/1181 |
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