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REFERENCES
1. Guo Y, Wang X, Yuan Q, Liu S, Liu S. Transition characteristics of driver’s intentions triggered by emotional evolution in two-lane urban
roads. IET Intell Trans Syst 2020;14:1788-98. DOI
2. Rokonuzzaman M, Mohajer N, Nahavandi S, Mohamed S. Review and performance evaluation of path tracking controllers of autonomous
vehicles. IET Intell Trans Syst 2021;15:646-70. DOI
3. Sun Z, Bebis G, Miller R. Monocular precrash vehicle detection: features and classifiers. IEEE Trans Image Process 2006;15:2019-34.
DOI
4. Halmaoui H, Joulan K, Hautière N, Cord A, Brémond R. Quantitative model of the driver’s reaction time during daytime fog - application
to a head up display-based advanced driver assistance system. IET Intell Trans Syst 2015;9:375-81. DOI
5. Wang Y, Xie W, Liu H. Low-light image enhancement based on deep learning: a survey. Opt Eng 2022;61:040901. DOI
6. Altaf MA, Ahn J, Khan D, Kim MY. Usage of IR sensors in the HVAC systems, vehicle and manufacturing industries: a review. IEEE
Sens J 2022;22:9164-76. DOI
7. Chen B, Wang W, Qin Q. Robust multi-stage approach for the detection of moving target from infrared imagery. Opt Eng 2012;51:067006.
DOI
8. Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision
and pattern recognition (CVPR’05); 2005 Jun 20-25; San Diego, CA, USA. IEEE; 2005. pp. 886-93. DOI
9. Girshick R, Donahue J, Darrell T, Malik J. Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014
IEEE Conference on Computer Vision and Pattern Recognition; 2014 Jun 23-28; Columbus, OH, USA. IEEE; 2014. pp. 580-87. DOI
10. Redmon J, Divvala S, Girshick R, Farhadi A. You only look once: unified, real-time object detection. In: 2016 IEEE Conference on
Computer Vision and Pattern Recognition (CVPR); 2016 Jun 27-30; Las Vegas, NV, USA. IEEE; 2016. pp. 779-88. DOI
11. Law H, Deng J. Cornernet: Detecting objects as paired keypoints. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y, editors. Computer
Vision – ECCV 2018). Cham: Springer; 2018. pp. 765-81. DOI
12. Kuang X, Sui X, Liu Y, Chen Q, Gu G. Single infrared image enhancement using a deep convolutional neural network. Neurocomputing
2019;332:119-28. DOI
13. Li J, Liang X, Shen S, Xu T, Feng J, Yan S. Scale-aware fast R-CNN for pedestrian detection. IEEE Trans Multimed 2017;20:985-96.
DOI
14. Fan R, Wang H, Wang Y, Liu M, Pitas I. Graph attention layer evolves semantic segmentation for road pothole detection: a benchmark
and algorithms. IEEE Trans Image Process 2021;30:8144-54. DOI
15. Feng H, Wang X, Feng M, Bu C. A lane segmentation and traffic object detection multi-task neural network for AR-HUD. In: 2021 China
Automation Congress (CAC); 2021 Oct 22-24; Beijing, China. IEEE; 2021. pp. 3062-67. DOI
16. Li Y, Wang H, Dang LM, Nguyen TN, Han D, Moon H. A deep learning-based hybrid framework for object detection and recognition in
autonomous driving. IEEE Access 2020;8:194228-39. DOI
17. Wang C, Luo D, Liu Y, Xu B, Zhou Y. Near-surface pedestrian detection method based on deep learning for UAVs in low illumination
environments. Opt Eng 2022;61:023103. DOI
18. Liu R, Liu E, Yang J, Zhang T, Cao Y. Point target detection of infrared images with eigentargets. Opt Eng 2007;46:110502. DOI
19. Han J, Yu Y, Liang K, Zhang H. Infrared small-target detection under complex background based on subblock-level ratio-difference joint
local contrast measure. Opt Eng 2018;57:103105. DOI
20. Park J, Chen J, Cho YK, Kang DY, Son BJ. CNN-based person detection using infrared images for night-time intrusion warning systems.
Sensors 2020;20:34. DOI
21. Cao Y, Zhou T, Zhu X, Su Y. Every feature counts: an improved one-stage detector in thermal imagery. In: 2019 IEEE 5th International
Conference on Computer and Communications (ICCC); 2019 Dec 6-9; Chengdu, China. IEEE; 2019. pp. 1965-9. DOI
22. Hao S, Gao S, Ma X, He T. Anchor-free infrared pedestrian detection based on cross-scale feature fusion and hierarchical attention
mechanism. Infrared Phys Technol 2023;131:104660. DOI
23. Zhang H, Fromont E, Lefevre S, Avignon B. Multispectral fusion for object detection with cyclic fuse-and-refine blocks. In: 2020 IEEE
International Conference on Image Processing (ICIP); 2020 Oct 25-28; Abu Dhabi, United Arab Emirates. IEEE; 2020. pp. 276-80. DOI
24. Du S, Zhang P, Zhang B, Xu H. Weak and occluded vehicle detection in complex infrared environment based on improved YOLOv4.
IEEE Access 2021;9:25671-80. DOI
25. Narayanan A, Kumar RD, RoselinKiruba R, Sharmila TS. Study and analysis of pedestrian detection in thermal images using YOLO and
SVM. In: 2021 Sixth International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET); 2021 Mar
25-27; Chennai, India. IEEE; 2021. pp. 431-34. DOI
26. Liao Z, Zhao Y, Huang X, Wu J. CMF net: detecting objects in infrared traffic image with combination of multiscale features. In: 2021
IEEE Global Communications Conference (GLOBECOM); 2021 Dec 7-11; Madrid, Spain. IEEE; 2021. pp. 1-6. DOI
27. Zhang ZD, Tan ML, Lan ZC, Liu HC, Pei L, Yu WX. CDNet: a real-time and robust crosswalk detection network on Jetson nano based
on YOLOv5. Neural Comput Appl 2022;34:10719-30. DOI
28. Jayasinghe O, Hemachandra S, Anhettigama D, et al. Towards real-time traffic sign and traffic light detection on embedded systems. In:
2022 IEEE Intelligent Vehicles Symposium (IV); 2022 Jun 4-9; Aachen, Germany. IEEE; 2022. pp. 723-28. DOI
29. Ren S, He K, Girshick R, Sun J. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern
Anal Mach 2015;39:1137-49. DOI
30. Bochkovskiy A, Wang CY, Liao HYM. Yolov4: optimal speed and accuracy of object detection. arXiv. [Preprint.] Apr 23, 2020 [accessed
2024 Sep 14]. Available from: https://arxiv.org/abs/2004.10934.