Vehicle-camel collisions in Saudi Arabia: Application of single and multi-stage deep learning object detectors
Vehicle-camel collision is a persistent issue in countries where population of camels is high such as Saudi Arabia. The purpose of the research is to introduce a new solution to eliminate this issue. Previous solutions, such as fencing the sides of the roads, designing better camel warning signs and...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | Ain Shams Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447923002174 |
| _version_ | 1797345936185229312 |
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| author | Saleh Alghamdi Abdullah Algethami Ting Tan |
| author_facet | Saleh Alghamdi Abdullah Algethami Ting Tan |
| author_sort | Saleh Alghamdi |
| collection | DOAJ |
| description | Vehicle-camel collision is a persistent issue in countries where population of camels is high such as Saudi Arabia. The purpose of the research is to introduce a new solution to eliminate this issue. Previous solutions, such as fencing the sides of the roads, designing better camel warning signs and fining camel owners when camels cross high traffic roads, are either expensive, ineffective, or hard to implement. Therefore, in this work, we harness the power of deep learning to tackle this problem. In particular, we use state-of-the-art deep learning object detectors to detect camels on roads with high accuracy. Results show that all implemented models were capable of detecting camels on or near roads. Moreover, the single-stage detector Yolo v3 was found to be the most accurate and is as fast as its successor Yolo v4. Findings of this work helped select the deep learning model needed for a reliable and automatic vehicle-camel collision avoidance system. |
| first_indexed | 2024-03-08T11:26:01Z |
| format | Article |
| id | doaj.art-a805ea7cddaf4ab6a6c7e7caf845a772 |
| institution | Directory Open Access Journal |
| issn | 2090-4479 |
| language | English |
| last_indexed | 2024-03-08T11:26:01Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj.art-a805ea7cddaf4ab6a6c7e7caf845a7722024-01-26T05:33:03ZengElsevierAin Shams Engineering Journal2090-44792024-01-01151102328Vehicle-camel collisions in Saudi Arabia: Application of single and multi-stage deep learning object detectorsSaleh Alghamdi0Abdullah Algethami1Ting Tan2Department of Civil Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia; Corresponding author.Department of Mechanical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaDepartment of Civil Engineering, Sun Yat-Sen University, 519082, ChinaVehicle-camel collision is a persistent issue in countries where population of camels is high such as Saudi Arabia. The purpose of the research is to introduce a new solution to eliminate this issue. Previous solutions, such as fencing the sides of the roads, designing better camel warning signs and fining camel owners when camels cross high traffic roads, are either expensive, ineffective, or hard to implement. Therefore, in this work, we harness the power of deep learning to tackle this problem. In particular, we use state-of-the-art deep learning object detectors to detect camels on roads with high accuracy. Results show that all implemented models were capable of detecting camels on or near roads. Moreover, the single-stage detector Yolo v3 was found to be the most accurate and is as fast as its successor Yolo v4. Findings of this work helped select the deep learning model needed for a reliable and automatic vehicle-camel collision avoidance system.http://www.sciencedirect.com/science/article/pii/S2090447923002174Object detectionVehicle-camel collisionYolo v3Yolo v4 |
| spellingShingle | Saleh Alghamdi Abdullah Algethami Ting Tan Vehicle-camel collisions in Saudi Arabia: Application of single and multi-stage deep learning object detectors Ain Shams Engineering Journal Object detection Vehicle-camel collision Yolo v3 Yolo v4 |
| title | Vehicle-camel collisions in Saudi Arabia: Application of single and multi-stage deep learning object detectors |
| title_full | Vehicle-camel collisions in Saudi Arabia: Application of single and multi-stage deep learning object detectors |
| title_fullStr | Vehicle-camel collisions in Saudi Arabia: Application of single and multi-stage deep learning object detectors |
| title_full_unstemmed | Vehicle-camel collisions in Saudi Arabia: Application of single and multi-stage deep learning object detectors |
| title_short | Vehicle-camel collisions in Saudi Arabia: Application of single and multi-stage deep learning object detectors |
| title_sort | vehicle camel collisions in saudi arabia application of single and multi stage deep learning object detectors |
| topic | Object detection Vehicle-camel collision Yolo v3 Yolo v4 |
| url | http://www.sciencedirect.com/science/article/pii/S2090447923002174 |
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