Abstract
Public highways are, in reality, the cornerstone of the country's transportation system. Accidents are unavoidable with this mode of transportation. Collisions involving resting livestock on national highways occur in most countries around the world. It endangers both the drivers and the animals. This paper proposes a method for mitigating the risk of accidents caused by deceased animals, notably cattle that are generating traffic and congestion on national highways and may constitute a safety risk. We have proposed an Internet of Things (IoT) fog-based framework for reclining livestock identification techniques for roadways, data are collected using the IoT-enabled video recording surveillance cameras. We use feature extraction, characteristic expression, assessment criteria, and an unrestricted approach for detecting deceased livestock (such as cows or buffalos), as well as recommendations on whether their placement is harmful to highway traffic. In this study, you only look once (YOLO) image recognition algorithm is implemented for reclining cattle on roadways using the fog layer for training and evaluating datasets. The performance parameters of the proposed framework, such as accuracy, recall, precision, mean average precision (mAP), and interference time, have been measured, and a comparison with existing state-of-the-art techniques has been presented. The obtained findings indicate that the suggested framework surpasses the present approaches, with a higher accuracy of 98% and an interference time of 4.68 ms. Artificially intelligent surveillance system can spot reclined livestock utilizing surveillance videos on roadways. This will ensure passenger safety as well as the safety of roadside cattle.
Similar content being viewed by others
Availability of data and materials
All the datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.
References
Ashik MH, Maswood MMS, Alharbi AG (2020) Designing a fog-cloud architecture using blockchain and analyzing security improvements. In: 2020 international conference on electrical, communication, and computer engineering (ICECCE) (pp. 1–6). IEEE, New York.
Ameratunga S, Hijar M, Norton R (2006) Road-traffic injuries: confronting disparities to address a global-health problem. The Lancet 367(9521):1533–1540
Ayman Mounir SE, Hussein WH, Karam O (2021). Accident detection and road monitoring in real time using deep learning and lane detection algorithms. In: 2021 The 4th International Conference on Machine Learning and Machine Intelligence, pp. 30–37.
Azmi M, Nasu SA, Kasim AM, Ariefiandy A, Purwandana D, Ciofi C, Jessop TS (2021) Incidences of road kills and injuries of Komodo Dragons along the North Coast of Flores Island, Indonesia. Herpetol Conserv Biol 16(1):11–16
Baker T, Asim M, MacDermott Á, Iqbal F, Kamoun F, Shah B, Alfandi O, Hammoudeh M (2020) A secure fog‐based platform for SCADA‐based IoT critical infrastructure. Softw Pract Experience 50(5):503–518.
Baviskar A (2020) Cows, cars and cycle-rickshaws: bourgeois environmentalists and the battle for Delhi's streets. In: Elite and everyman. Routledge, India, pp 391–418.
Biljecki F, Stoter J, Ledoux H, Zlatanova S, Çöltekin A (2015) Applications of 3D city models: state of the art review. ISPRS Int J Geo Inf 4(4):2842–2889
Buric M, Pobar M, Ivasic-Kos M (2018) Ball detection using YOLO and Mask R-CNN. In: 2018 International conference on computational science and computational intelligence (CSCI), pp 319–323. IEEE, Newn York.
Chen Y, Zhang C, Qiao T, Xiong J, Liu B (2021) Ship detection in optical sensing images based on YOLOv5. In: Twelfth international conference on graphics and image processing (ICGIP 2020). SPIE, Vol. 11720, pp. 102–106.
Dai J, Li Y, He K, Sun J (2016) R-fcn: Object detection via region-based fully convolutional networks. Adv Neural Inform Proceess Syst, 29.
de Oliveira RP, Achcar JA (2020) Victims of road accidents with serious injuries and dependence on some individual, climatic and infrastructure factors on federal highways in Brazil. Int J Injury Control Safety Promot 27(3):355–361
Du Y, Pan N, Xu Z, Deng F, Shen Y, Kang H (2021) Pavement distress detection and classification based on YOLO network. Int J Pavement Eng 22(13):1659–1672
Eboli L, Forciniti C, Mazzulla G (2020) Factors influencing accident severity: an analysis by road accident type. Transport Res Proc 47:449–456
Grace M (2017) The behavior of humans and wildlife with respect to roads: insights for mitigation and management [Doctoral dissertation, University of Central Florida]. Electronic Theses and Dissertations. 5375. https://stars.library.ucf.edu/etd/5375
Gupta S, Zhang W, Wang F (2016) Model accuracy and runtime tradeoff in distributed deep learning: A systematic study. In: 2016 IEEE 16th International conference on data mining (ICDM). IEEE, New York, pp. 171–180.
Haq MT, Zlatkovic M, Ksaibati K (2021) Assessment of commercial truck driver injury severity based on truck configuration along a mountainous roadway using hierarchical Bayesian random intercept approach. Accid Anal Prev 162:106392
Harris M, Bose NK, Klass M, Mencher JP, Oberg K, Opler MK et al (1966) The cultural ecology of India's sacred cattle [and comments and replies]. Curr Anthropol 7(1):51–66.
Huang R, Pedoeem J, Chen C (2018) YOLO-LITE: a real-time object detection algorithm optimized for non-GPU computers. In: 2018 IEEE International Conference on Big Data (Big Data). IEEE, New York, pp. 2503–2510.
Huang F, Wang BW, Li QP, Zou J (2021) Texture surface defect detection of plastic relays with an enhanced feature pyramid network. J Intell Manufact, 1–17.
Huu PN, Pham Thi Q, Tong Thi Quynh P (2022) Proposing lane and obstacle detection algorithm using YOLO to control self-driving cars on advanced networks. Adv Multimedia 2022:3425295
Hosseini-Asl E, McCann B, Wu CS, Yavuz S, Socher R (2020) A simple language model for task-oriented dialogue. Adv Neural Inf Process Syst 33:20179–20191
Jiang G, Yi X, Xiaotong G, Shanshang G, Xiaoqing S, Ruoyu Z, Liming W, Yuqiong W (2022) An obstacle detection and distance measurement method for sloped roads based on VIDAR. J Robot 2022:5264347
Khan A, Gupta S, Gupta SK (2020) Multi-hazard disaster studies: Monitoring, detection, recovery, and management, based on emerging technologies and optimal techniques. Int J Disas Risk Reduct 47:101642
Khan NA, Jhanjhi NZ, Brohi SN, Usmani RSA, Nayyar A (2020) Smart traffic monitoring system using unmanned aerial vehicles (UAVs). Comput Commun 157:434–443
Ku J, Mozifian M, Lee J, Harakeh A, Waslander SL (2018) Joint 3d proposal generation and object detection from view aggregation. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, New York, pp. 1–8.
Laroca R, Severo E, Zanlorensi LA, Oliveira LS, Gonçalves GR, Schwartz WR, Menotti D (2018) A robust real-time automatic license plate recognition based on the YOLO detector. In: 2018 international joint conference on neural networks (ijcnn). IEEE, New York, pp. 1–10.
Lee C, Kim H, Oh S, Doo I (2021) A study on building a “real-time vehicle accident and road obstacle notification model” using AI CCTV. Appl Sci 11(17):8210
Lella KK, Pja A (2022) Automatic diagnosis of COVID-19 disease using deep convolutional neural network with multi-feature channel from respiratory sound data: cough, voice, and breath. Alex Eng J 61(2):1319–1334
Levi D, Garnett N, Fetaya E, Herzlyia I (2015) StixelNet: A Deep Convolutional Network for Obstacle Detection and Road Segmentation. In: BMVC (Vol. 1, No. 2, p. 4).
Li H, Ota K, Dong M (2018) Learning IoT in edge: deep learning for the Internet of Things with edge computing. IEEE Network 32(1):96–101
Li J, Qu C, Shao J (2017) Ship detection in SAR images based on an improved faster R-CNN. In: 2017 SAR in big data era: models, methods and applications (BIGSARDATA). IEEE, New York, pp. 1–6
Li Z, Peng C, Yu G, Zhang X, Deng Y, Sun J (2017) Light-head r-cnn: In defense of two-stage object detector. arXiv preprint arXiv:1711.07264.
Li G, Fu L, Gao C, Fang W, Zhao G, Shi F, Dhupia J, Zhao K, Li R, Cui Y (2022) Multi-class detection of kiwifruit flower and its distribution identification in orchard based on YOLOv5l and Euclidean distance. Comput Electron Agric 201:107342
Lin P (2016). Why ethics matters for autonomous cars. In: Autonomous driving. Springer, Berlin, pp. 69–85.
Liu H, Chen S, Kubota N (2013) Intelligent video systems and analytics:a survey. IEEE Trans Industr Inf 9(3):1222–1233
Liu L, Ouyang W, Wang X, Fieguth P, Chen J, Liu X, Pietikäinen M (2020) Deep learning for generic object detection: a survey. Int J Comput Vis 128(2):261–318
Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssd: Single shot multibox detector. In: European conference on computer vision. Springer, Cham, pp. 21–37.
Manivasakan H, Kalra R, O’Hern S, Fang Y, Xi Y, Zheng N (2021) Infrastructure requirement for autonomous vehicle integration for future urban and suburban roads–Current practice and a case study of Melbourne, Australia. Transport Res Part A: Policy Pract 152:36–53
Mehdian M, Mirzahossein H, Abdi Kordani A (2022) A data-driven functional classification of urban roadways based on geometric design, traffic characteristics, and land use features. J Adv Transp 2022:9970464
Machado P, Matic I, de Lemos F, Ihianle IK, Adama DA (2022) Estimating the power consumption of heterogeneous devices when performing AI Inference. arXiv preprint arXiv:2207.06150.
Malo JE, Suárez F, Díez A (2004) Can we mitigate animal–vehicle accidents using predictive models? J Appl Ecol 41(4):701–710
Maeda H, Sekimoto Y, Seto T, Kashiyama T, Omata H (2018) Road damage detection using deep neural networks with images captured through a smartphone. arXiv preprint arXiv:1801.09454.
Mehta Y, Pai MM, Mallissery S, Singh S (2016) Cloud enabled air quality detection, analysis and prediction-a smart city application for smart health. In: 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC). IEEE, New York, pp. 1–7.
Menon A, Omman B, Asha S (2021) Pedestrian counting using Yolo V3. In: 2021 International Conference on Innovative Trends in Information Technology (ICITIIT). IEEE, New York, pp. 1–9.
Mohanty CR, Radhakrishnan RV, Jain M, Sasmal PK, Hansda U, Vuppala SK, Doki SK (2021) A study of the pattern of injuries sustained from road traffic accidents caused by impact with stray animals. J Emerg Trauma Shock 14(1):23
Mollah MB, Islam KR, Islam SS (2012) Next generation of computing through cloud computing technology. In: 2012 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, New York, pp. 1–6.
Ölmez E, Akdoğan V, Korkmaz M, Er O (2020) Automatic segmentation of meniscus in multispectral MRI using regions with convolutional neural network (R-CNN). J Digit Imaging 33(4):916–929
O’Neill B, Mohan D (2002) Reducing motor vehicle crash deaths and injuries in newly motorising countries. BMJ 324(7346):1142–1145
Pratikakis I, Zagoris K, Barlas G, Gatos B (2017) ICDAR2017 competition on document image binarization (DIBCO 2017). In: 2017 14th IAPR International conference on document analysis and recognition (ICDAR). IEEE, New York, Vol. 1, pp. 1395–1403.
Samann FE, Abdulazeez AM, Askar S (2021) Fog computing based on machine learning: a review. Int J Interactive Mobile Technol 15(12).
Sarmah T, Das S, Narendr A, Aithal BH (2020) Assessing human vulnerability to urban flood hazard using the analytic hierarchy process and geographic information system. Int J Disast Risk Reduct 50:101659
Schlagloth R, Santamaria F, Melzer A, Keatley MR, Houston W (2022) Vehicle collisions and dog attacks on Victorian koalas as evidenced by a retrospective analysis of sightings and admission records 1997–2011. Aust Zool 42(3):655–666
Shotton J, Winn J, Rother C, Criminisi A (2009) Textonboost for image understanding: multi-class object recognition and segmentation by jointly modeling texture, layout, and context. Int J Comput Vis 81(1):2–23
Simoons FJ, Batra SM, Chakravarti AK, Diener P, Ferro-Luzzi GE, Harris M et al. (1979). Questions in the sacred-cow controversy [and comments and reply]. Curr Anthropol 20(3):467–493.
Tan M, Pang R, Le QV (2020) Efficientdet: Scalable and efficient object detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 10781–10790.
Thuan D (2021) Evolution of Yolo algorithm and Yolov5: The State-of-the-Art object detection algorithm [Bachelor’s Thesis, Oulu University of Applied Sciences]. https://www.theseus.fi/handle/10024/452552
Uddin W (2022) Mobile and area sources of greenhouse gases and abatement strategies. Handbook of Climate Change Mitigation and Adaptation. Springer International Publishing, Cham, pp 743–807
Vijayakumar V, Nedunchezhian R (2012) A study on video data mining. Int J Multimedia Inform Retrieval 1(3):153–172
Wang L, Zhao X, Sun J, Zhang Y, Zhang H, Yu T, Liu Y (2023) StyleAvatar: Real-time Photo-realistic Portrait Avatar from a Single Video. arXiv preprint arXiv:2305.00942.
Womg A, Shafiee MJ, Li F, Chwyl B (2018) Tiny SSD: A tiny single-shot detection deep convolutional neural network for real-time embedded object detection. In: 2018 15th Conference on Computer and Robot Vision (CRV). IEEE, New York, pp. 95–101.
Zaghari N, Fathy M, Jameii SM, Shahverdy M (2021) The improvement in obstacle detection in autonomous vehicles using YOLO non-maximum suppression fuzzy algorithm. J Supercomput 77(11):13421–13446
Acknowledgements
Not Applicable.
Funding
None.
Author information
Authors and Affiliations
Contributions
All authors have contributed equally.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Saini, M., Singh, H., Sengupta, E. et al. An intelligent machine learning-enabled cattle reclining risk mitigation technique using surveillance videos. Neural Comput & Applic 36, 2029–2047 (2024). https://doi.org/10.1007/s00521-023-09143-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-023-09143-2