Abstract
This research endeavors to tackle the pressing issue of rear-end collisions by introducing a comprehensive brake light recognition system. The primary focus is on developing a cost-effective and highly accurate solution, leveraging the YOLOv7 algorithm for the classification and detection of rear lights. Complemented by artificial intelligence (AI), the system incorporates an alert warning mechanism to notify distracted drivers promptly. The integration of an infrared (IR) sensor further enhances the system's capabilities, allowing precise measurement of the distance between vehicles. Motivated by the imperative to enhance road safety, the proposed methodology employs deep learning through YOLOv7, facilitating the real-time identification of brake lights. The subsequent application of an artificial intelligent deep learning (AIDL) model ensures proactive alert signals when a distracted driver's vehicle approaches within an extremely short distance (75 cm) of another vehicle. Noteworthy findings underscore the system's effectiveness in monitoring and responding to brake lights, showcasing its potential to prevent collisions from behind. The IR sensor's inclusion refines the precision of the alert system, demonstrating its robust performance in real-world scenarios. Overall, this research contributes a holistic brake light recognition system that combines YOLOv7, AI, and an IR sensor, showcasing its viability in significantly reducing accidents associated with distracted driving and rear-end collisions, thereby emphasizing the critical role of advanced technologies in proactively addressing road safety concerns.












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References
Wang JG, Zhou L, Pan Y, Lee S, Song Z, Han BS, Saputra VB (2016) Appearance-based brake-lights recognition using deep learning and vehicle detection. In 2016 IEEE Intelligent Vehicles Symposium (IV) (pp. 815–820). IEEE
Kumar G, Rampavan M, Ijjina EP, (2021) Deep Learning based Brake Light Detection for Two Wheelers. In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1–4). IEEE
Chen HT, Wu YC, Hsu CC (2015) Daytime preceding vehicle brake light detection using monocular vision. IEEE Sens J 16(1):120–131
Jagtap SD, Kanade SS (2022) Exception Model for Brake Light Detection for Autonomous Vehicles. In 2022 2nd International Conference on Intelligent Technologies (CONIT) (pp. 1–6). IEEE
Rajendar S, Rathinasamy D, Pavithra R, Kaliappan VK, Gnanamurthy S (2022) Prediction of stopping distance for autonomous emergency braking using stereo camera pedestrian detection. Mater Today Proc 51:1224–1228
Li Q, Garg S, Nie J, Li X, Liu RW, Cao Z, Hossain MS (2020) A highly efficient vehicle taillight detection approach based on deep learning. IEEE Trans Intell Transp Syst 22(7):4716–4726
Satzoda RK, Trivedi MM (2016) Looking at vehicles in the night: Detection and dynamics of rear lights. IEEE Trans Intell Transp Syst 20(12):4297–4307
Wang X, Liu J, Qiu T, Mu C, Chen C, Zhou P (2020) A real-time collision prediction mechanism with deep learning for intelligent transportation system. IEEE Trans Veh Technol 69(9):9497–9508
Chen DY, Lin TY, Chen GR (2019) Robust Vision-Based Daytime Vehicle Brake Light Detection Using Two-Stage Deep Learning Model. In Proceedings of the 3rd International Conference on Big Data and Internet of Things (pp. 47–50)
Feng JK, Chiang ML, Chuang SH, Fang CY, Chen SW (2018) Forward vehicle deceleration detection system for motorcycle at nighttime. In 2018 IEEE 4th International Conference on Computer and Communications (ICCC) (pp. 515–521). IEEE
Almagambetov A, Velipasalar S, Casares M (2015) Robust and computationally lightweight autonomous tracking of vehicle taillights and signal detection by embedded smart cameras. IEEE Trans Industr Electron 62(6):3732–3741
Chen DY, Peng YJ, Chen LC, Hsieh JW (2014) Nighttime turn signal detection by scatter modeling and reflectance-based direction recognition. IEEE Sens J 14(7):2317–2326
Do TH, Tran DK, Hoang DQ, Vuong D, Hoang TM, Dao NN, Lee C, Cho S (2021). A Novel Algorithm for Estimating Fast-Moving Vehicle Speed in Intelligent Transport Systems. In 2021 International Conference on Information Networking (ICOIN) (pp. 499–503). IEEE
Wu Y, Geng K, Xue P, Yin G, Zhang N, Lin Y, (2019). Traffic lights detection and recognition algorithm based on multi-feature fusion. In 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC) (pp. 427–432). IEEE
Pavani K, Sriramya P (2022) Comparison of KNN, ANN, CNN and YOLO algorithms for detecting the accurate traffic flow and build an Intelligent Transportation System. In 2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) (Vol. 2, pp. 628–633). IEEE
Liu S, Wang Y, Yu Q, Liu H, Peng Z (2022) CEAM-YOLOv7: Improved YOLOv7 based on channel expansion and attention mechanism for driver distraction behavior detection. IEEE Access 10:129116–129124
Lee KH, Tagawa T, Pan JEM, Gaidon A, Douillard B (2019) An attention-based recurrent convolutional network for vehicle taillight recognition. In 2019 IEEE Intelligent Vehicles Symposium (IV) (pp. 2365–2370). IEEE
Zhang X, Story B, Rajan D (2021) Night time vehicle detection and tracking by fusing vehicle parts from multiple cameras. IEEE Trans Intell Transp Syst 23(7):8136–8156
Guo F, Jiang Z, Wang Y, Chen C, Qian Y (2022) Dense traffic detection at highway-railroad grade crossings. IEEE Trans Intell Transp Syst 23(9):15498–15511
“Vehicle rear signal dataset,” 2017. Available: http://www.vllab1.ucmerced.edu/∼hhsu22/rearsignal/rearsignal
Shang J, Guan HP, Liu Y, Bi H, Yang L, Wang M (2021) A novel method for vehicle headlights detection using salient region segmentation and PHOG feature. Multimed Tools Appl 80:22821–22841
Reddy ESTK, Rajaram V (2022) Pothole Detection using CNN and YOLO v7 Algorithm. In 2022 6th International Conference on Electronics, Communication and Aerospace Technology (pp. 1255–1260). IEEE
Wang Z, Huo W, Yu P, Qi L, Geng S, Cao N (2019) Performance evaluation of region-based convolutional neural networks toward improved vehicle taillight detection. Appl Sci 9(18):3753
Rapson CJ, Seet BC, Naeem MA, Lee JE, Klette R (2019) A Performance Comparison of Deep Learning Methods for Real-time Localisation of Vehicle Lights in Video Frames. In 2019 IEEE Intelligent Transportation Systems Conference (ITSC) (pp. 567–572). IEEE
Kushwaha M (2021) Analysis and identifying of important features on road accidents by using machine learning algorithms. 한국감성과학회 국제학술대회 (ICES), 2021 p 110–113
Kushwaha M, Abirami MS (2021) Comparative Analysis on the Prediction of Road Accident Severity Using Machine Learning Algorithms. International Conference on Micro-Electronics and Telecommunication Engineering. Springer Nature Singapore, Singapore, pp 269–280
Abirami MS, Vennila B, Chilukalapalli EL, Kuriyedath R (2021) A classification model to predict onset of smoking and drinking habits based on socio-economic and sociocultural factors. J Ambient Intell Humaniz Comput 12:4171–4179
Abirami MS, Vennila B, Suganthi K, Kawatra S, Vaishnava A (2022) Detection of choroidal neovascularization (CNV) in retina OCT images using VGG16 and DenseNet CNN. Wireless Personal Communications, pp.1–15
Pu Z, Cui Z, Tang J, Wang S, Wang Y (2021) Multimodal traffic speed monitoring: A real-time system based on passive Wi-Fi and Bluetooth sensing technology. IEEE Internet Things J 9(14):12413–12424
Nava D, Panzani G, Savaresi SM (2019) A collision warning oriented brake lights detection and classification algorithm based on a mono camera sensor. In 2019 IEEE Intelligent Transportation Systems Conference (ITSC) (pp. 319–324). IEEE
Singh D, Kaur M, Jabarulla MY, Kumar V, Lee HN (2022) Evolving fusion-based visibility restoration model for hazy remote sensing images using dynamic differential evolution. IEEE Trans Geosci Remote Sens 60:1–14
Singh D, Kumar V (2018) Defogging of road images using gain coefficient-based trilateral filter. J Electron Imaging 27(1):013004–013004
Singh D, Kumar V (2019) Single image defogging by gain gradient image filter. Sci China Inf Sci 62:1–3
Feng D, Haase-Schütz C, Rosenbaum L, Hertlein H, Glaeser C, Timm F, Wiesbeck W, Dietmayer K (2020) Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges. IEEE Trans Intell Transp Syst 22(3):1341–1360
Hsu HK, Tsai YH, Mei X, Lee KH, Nagasaka N, Prokhorov D, Yang MH (2017) Learning to tell brake and turn signals in videos using cnn-lstm structure. In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) (pp. 1–6). IEEE
Sheng M, Liu C, Zhang Q, Lou L, Zheng Y (2018) Vehicle detection and classification using convolutional neural networks. In 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) (pp. 581–587). IEEE
Yang Y, Luo H, Xu H, Wu F (2015) Towards real-time traffic sign detection and classification. IEEE Trans Intell Transp Syst 17(7):2022–2031
Xiang Y, Huang S, Li M, Li J, Wang W (2019) Rear-end collision avoidance-based on multi-channel detection. IEEE Trans Intell Transp Syst 21(8):3525–3535
Yang W, Wan B, Qu X (2020) A forward collision warning system using driving intention recognition of the front vehicle and V2V communication. IEEE Access 8:11268–11278
Chiranjeevi P, Rajaram A (2023) A lightweight deep learning model based recommender system by sentiment analysis. Journal of Intelligent & Fuzzy Systems (Preprint) p 1–14
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Kushwaha, M., Abirami, M.S. Forward vehicle brake light detection for avoiding road accidents using Yolov7 and IR sensor. Multimed Tools Appl 83, 86339–86357 (2024). https://doi.org/10.1007/s11042-024-19427-x
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DOI: https://doi.org/10.1007/s11042-024-19427-x