Object Detection using Infrared Systems During Fog and Rain
| International Journal of Computer Science and Engineering |
| © 2023 by SSRG - IJCSE Journal |
| Volume 10 Issue 8 |
| Year of Publication : 2023 |
| Authors : Ujjwal Rastogi |
10.14445/23488387/IJCSE-V10I8P101
How to Cite?
Ujjwal Rastogi, "Object Detection using Infrared Systems During Fog and Rain," SSRG International Journal of Computer Science and Engineering , vol. 10, no. 8, pp. 1-7, 2023. Crossref, https://doi.org/10.14445/23488387/IJCSE-V10I8P101
Abstract:
This research study focuses on the utilization of infrared systems for object detection during fog and rain, aiming to improve detection accuracy in adverse weather conditions. Road accidents caused by foggy and misty weather conditions claimed 13,372 lives in 2021 in India, and another 25,360 were left injured — more than half were grievously injured. The current research has highlighted the challenges posed by fog and rain on traditional sensors and the potential of infrared technology to overcome these limitations. The rationale for this new research lies in the need for robust and reliable object detection in adverse weather scenarios, particularly for autonomous driving systems. The research methods involve designing and implementing an infrared-based object detection system, incorporating preprocessing techniques, and employing object detection algorithms and frameworks. The study's major findings demonstrate the potential of infrared systems in enhancing object detection reliability in foggy and rainy conditions, as evaluated using real-world datasets. The significance of this study lies in its contribution to improving road safety by providing valuable insights into the utilization of infrared technology for object detection in adverse weather scenarios.
Keywords:
Object detection, Infrared system, Fog, Rain, Adverse weather conditions.
References:
[1] Santokh Singh, “Critical Reasons for Crashes Investigated in the National Motor Vehicle Crash Causation Survey,” National Highway Traffic Safety Administration, pp. 1-3, 2015.
[Google Scholar] [Publisher Link]
[2] Mourad A. Kenk, and Mahmoud Hassaballah, “DAWN: Vehicle Detection in Adverse Weather Nature Dataset,” ArXiv, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Răzvan-Cătălin Miclea et al., “Visibility Enhancement and Fog Detection: Solutions Presented in Recent Scientific Papers with Potential for Application to Mobile Systems,” Sensors, vol. 21, no. 10, pp. 1-39, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Ministry of Road Transport and Highways Transport Research Wing, Road Accident in India, 2021. [Online]. Available: https://morth.nic.in/sites/default/files/RA_2021_Compressed.pdf
[5] Hongjun Tan et al., “Infrared Sensation-Based Salient Targets Enhancement Methods in Low-Visibility Scenes,” Sensors, vol. 22, no. 15, pp. 1-16, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Jesse Levinson et al., “Towards Fully Autonomous Driving: Systems and Algorithms,” IEEE Symposium on Intelligent Vehicle, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Abhay Singh Bhadoriya, Vamsi Vegamoor, and Sivakumar Rathinam, “Vehicle Detection and Tracking Using Thermal Cameras in Adverse Visibility Conditions,” Sensors, vol. 22, no. 12, pp. 1-14, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Xianglin Meng et al., “YOLOv5s-Fog: An Improved Model Based on YOLOv5s for Object Detection in Foggy Weather Scenarios,” Sensors, vol. 23, no. 11, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Esmaeil Mirmahdi, and Omid Ghorbani Shirazi, “Installation of Suitable Sensors for Object Detection and Height Control on Combine Harvester,” SSRG International Journal of Mechanical Engineering, vol. 8, no. 5, pp. 12-19, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Didier Aubert et al., “Digital Imaging for Assessing and Improving Highway Visibility,” Transport Research Arena, pp. 1-10, 2014.
[Google Scholar] [Publisher Link]
[11] A. N. Rajagopalan, and Rama Chellappa, Motion Deblurring Algorithms and Systems, United Kingdom: Cambridge University Press, pp. 1-293, 2014.
[Google Scholar] [Publisher Link]
[12] Li Yang et al., “Image-Based Visibility Estimation Algorithm for Intelligent Transportation Systems,” IEEE Access, vol. 6, pp. 76728- 76740, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Silea Ioan, Miclea Razvan-Catalin, and Alexa Florin, “System for Visibility Distance Estimation in Fog Conditions based on Light Sources and Visual Acuity,” IEEE International Conference on Automation, Quality and Testing, Robotics, AQTR, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Ghulfam Zahra et al., “Visibility Enhancement of Scene Images Degraded by Foggy Weather Condition: An Application to Video Surveillance,” Computers Materials and Continua, vol. 68, no. 3, pp. 3465-3481, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Mate Krišto, Marina Ivasic-Kos, and Miran Pobar, “Thermal Object Detection in Difficult Weather Conditions Using YOLO,” IEEE Access, vol. 8, pp. 125459 – 125476, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[16] B. Shubha, and V. Veena Devi Shastrimath, “Study and Analysis of Thermal Imaging Sensors for Object Detection,” SSRG International Journal of Electronics and Communication Engineering, vol. 10, no. 5, pp. 38-48, 2023.
[CrossRef] [Publisher Link]
[17] Qasem Abu Al-Haija, Manaf Gharaibeh, and Ammar Odeh, “Detection in Adverse Weather Conditions for Autonomous Vehicles via Deep Learning,” Artificial Intelligence, vol. 3, no. 2, pp. 303-317, 2022.
[CrossRef] [Google Scholar] [Publisher Link]