[1] T. Koyama, K.M. Marshek, Toothed belt drives—past, present and future, Mechanism and Machine Theory, Vol. 23, pp. 227-241, 1988
[2] R. Perneder, I. Osborne, Handbook timing belts: principles, calculations, applications, Springer Science & Business Media, 2012
[3] N. Paul, C. Chung, Application of HDR algorithms to solve direct sunlight problems when autonomous vehicles using machine vision systems are driving into sun, Computers in Industry, Vol. 98, pp. 192-196, 2018
[4] H.A. Williams, M.H. Jones, M. Nejati, M.J. Seabright, J. Bell, N.D. Penhall, J.J. Barnett, M.D. Duke, A.J. Scarfe, H.S. Ahn, Robotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic arms, Biosystems Engineering, Vol. 181, pp. 140-156, 2019
[5] E. Pechenina, V. Pechenin, M. Bolotov, Neural network training procedure for machine vision systems in aerospace industries, AIP Conference Proceedings, AIP Publishing LLC, pp. 040014, 2021
[6] A. Martín, R. Lara-Cabrera, F. Fuentes-Hurtado, V. Naranjo, D. Camacho, EvoDeep: a new evolutionary approach for automatic deep neural networks parametrisation, Journal of Parallel and Distributed Computing, Vol. 117, pp. 180-191, 2018
[7] H. Yaşar, G. Çağıl, O. Torkul, M. Şişci, Cylinder Pressure Prediction of An HCCI Engine Using Deep Learning, Chinese Journal of Mechanical Engineering, Vol. 34, pp. 1-8, 2021
[8] N. Audebert, B. Le Saux, S. Lefèvre, Segment-before-detect: Vehicle detection and classification through semantic segmentation of aerial images, Remote Sensing, Vol. 9, pp. 368, 2017
[9] X. Wang, W. Zhang, X. Wu, L. Xiao, Y. Qian, Z. Fang, Real-time vehicle type classification with deep convolutional neural networks, Journal of Real-Time Image Processing, Vol. 16, pp. 5-14, 2019
[10] M.A. Rafique, W. Pedrycz, M. Jeon, Vehicle license plate detection using region-based convolutional neural networks, Soft Computing, Vol. 22, pp. 6429-6440, 2018
[11] J. Li, X. Shi, J. Wang, M. Yan, Adaptive road detection method combining lane line and obstacle boundary, IET Image Processing, Vol. 14, pp. 2216-2226, 2020
[12] S. Capela, R. Silva, S.R. Khanal, A.T. Campaniço, J. Barroso, V. Filipe, Engine Labels Detection for Vehicle Quality Verification in the Assembly Line: A Machine Vision Approach, Portuguese Conference on Automatic Control, Springer, pp. 740-751, 2020
[13] J. Rochussen, P. Kirchen, Robust image segmentation for feature extraction from internal combustion engine in-cylinder images, Measurement Science and Technology, Vol. 32, pp. 015302, 2020
[14] R. Girshick, J. Donahue, T. Darrell, J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 580-587, 2014
[15] J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You only look once: Unified, real-time object detection, Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779-788, 2016
[16] G. Jocher, A. Stoken, J. Borovec, NanoCode012, ChristopherSTAN, L, Changyu, Laughing, tkianai, A. Hogan, lorenzomammana, yxNONG, AlexWang1900, L. Diaconu, Marc, wanghaoyang0106, ml5ah, Doug, F. Ingham, Frederik, Guilhen, Hatovix, J. Poznanski, J. Fang, L. Yu, changyu98, M. Wang, N. Gupta, O. Akhtar, PetrDvoracek, and P. Rai,“ultralytics/yolov5: v3, 1 2020
[17] J.A. Kim, J.Y. Sung, S.H. Park, Comparison of Faster-RCNN, YOLO, and SSD for real-time vehicle type recognition, 2020 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), IEEE, pp. 1-4, 2020
[18] T.Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, C.L. Zitnick, Microsoft coco: Common objects in context, European conference on computer vision, Springer, pp. 740-75, 2014.
