Application of Computation Offloading in Edge Computing-Based Level Crossing Violation Detection Systems

  • Rian Putra Pratama Center for Smart Mechatronics, National Research and Innovation Agency, Bandung, Indonesia
  • Suhono Harso Supangkat School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia
Keywords: Computation Offloading, Edge Computing, Violation Detection Systems, Level Crossings, Intelligent Video Surveillance Systems


Level crossings remain a problem in several cities due to high violations. Currently, surveillance at level crossings is still performed conventionally. Since problems at level crossings are increasingly complex and conventional solutions are no longer effective, an intelligent video surveillance system is necessary. Intelligent video surveillance system implementation is a complex task and requires devices with extensive computing resources. This research aims to optimize the system for processing data in real-time by conducting computation near the data source and dividing computing tasks across several edge devices. This research proposes a solution in the form of an edge computing-based intelligent video surveillance system with a computation offloading method on limited devices. This research has two development stages. The initial stage involved developing an object detection model using a dataset of level crossings in Bandung City. The second stage was developing an edge computing-based system by applying the computation offloading method on limited computing devices. The edge computing method extends cloud computing to the network’s edge, enabling calculations near the data source. Conversely, the computation offloading method improves edge computing performance by dividing computing tasks. Results showed an increase in computing speed of around 1.5 times faster, with a violation detection accuracy rate reaching 89.4%. Additionally, GPU temperature decreased by 5.50 °C, GPU usage decreased by 44.05%, memory usage decreased by 301 Mb, and power consumption decreased by 2.28 W. The system developed is effective and efficient in optimizing the performance of the violation detection system in level crossings on limited computing devices.


“Lalu Lintas dan Angkutan Jalan,” Undang-Undang Republik Indonesia No. 22, 2009.

“Buku Statistik Bidang Perkeretaapian Tahun 2020,” The Directorate General of Railway, 2020.

A. Sianipar, “Kajian penerapan teknologi pintu dengan pagar otomatis dan yellow box di perlintasan sebidang,” J. Penelit. Transp. Darat., vol. 22, no. 1, pp. 91–102, Jun. 2020, doi: 10.25104/jptd.v22i1.1603.

S.H. Supangkat, A.A. Arman, R.A. Nugraha, and Y.A. Fatimah, “The implementation of Garuda Smart City Framework for smart city readiness mapping in Indonesia,” J. Asia-Pac. Stud., vol. 32, pp. 169–176, Mar. 2018, doi: 10.57278/wiapstokyu.32.0_169.

V. Tsakanikas and T. Dagiuklas, “Video surveillance systems-current status and future trends,” Comput. Elect. Eng., vol. 70, pp. 736–753, Aug. 2018, doi: 10.1016/j.compeleceng.2017.11.011.

R.P. Pratama and S.H. Supangkat, “Smart video surveillance system for level crossing: A systematic literature review,” 2021 Int. Conf. ICT Smart Soc. (ICISS), 2021, pp. 1–5, doi: 10.1109/ICISS53185.2021.9533222.

M.H. Kolekar, Intelligent Video Surveillance Systems An Algorithmic Approach. New York, NY, USA: CRC Press, 2017, doi: 10.1201/9781315153865.

A. Hampapur et al., “Smart video surveillance: Exploring the concept of multiscale spatiotemporal tracking,” IEEE Signal Process. Mag., vol. 22, no. 2, pp. 38–51, Mar. 2005, doi: 10.1109/MSP.2005.1406476.

G.F. Shidik et al., “A systematic review of intelligence video surveillance: Trends, techniques, frameworks, and datasets,” IEEE Access, vol. 7, pp. 170457–170473, Nov. 2019, doi: 10.1109/ACCESS.2019.2955387.

H. Sun, Y. Yu, K. Sha, and B. Lou, “mVideo: Edge computing based mobile video processing systems,” IEEE Access, vol. 8, pp. 11615–11623, Dec. 2020, doi: 10.1109/ACCESS.2019.2963159.

W. Yu et al., “A survey on the edge computing for the internet of things,” IEEE Access, vol. 6, pp. 6900–6919, Nov. 2018, doi: 10.1109/ACCESS.2017.2778504.

W. Shi et al., “Edge computing: Vision and challenges,” IEEE Internet Things J., vol. 3, no. 5, pp. 637–646, Oct. 2016, doi: 10.1109/JIOT.2016.2579198.

F. Wang et al., “Deep learning for edge computing applications: A state-of-the-art survey,” IEEE Access, vol. 8, pp. 58322–58336, Mar. 2020, doi: 10.1109/ACCESS.2020.2982411.

D.R. Patrikar and M.R. Parate, “Anomaly detection using edge computing in video surveillance system: Review,” Int. J. Multimed. Inf. Retr., vol. 11, no. 2, pp. 85–110, Jun. 2022, doi: 10.1007/s13735-022-00227-8.

A.C. Cob-Parro et al., “Smart video surveillance system based on edge computing,” Sensors, vol. 21, no. 9, pp. 1–20, May 2021, doi: 10.3390/s21092958.

A. Gupta and P. Prabhat, “Towards a resource efficient and privacy-preserving framework for campus-wide video analytics-based applications,” Complex Intell. Syst., vol. 9, no. 1, pp. 161–176, Feb. 2023, doi: 10.1007/s40747-022-00783-w.

M. Aazam, S. Zeadally, and K.A. Harras, “Offloading in fog computing for IoT: Review, enabling technologies, and research opportunities,” Future Gener. Comput. Syst., vol. 87, pp. 278–289, Oct. 2018, doi: 10.1016/j.future.2018.04.057.

A. Zaman, B. Ren, and X. Liu, “Artificial intelligence-aided automated detection of railroad trespassing,” Transp. Res. Rec., J. Transp. Res. Board,, vol. 2673, no. 7, pp. 25–37, Jul. 2019, doi: 10.1177/0361198119846468.

M.A.B. Fayyaz and C. Johnson, “Object detection at level crossing using deep learning,” Micromachines, vol. 11, no. 12, pp. 1–16, Dec. 2020, doi: 10.3390/mi11121055.

P. Sikora et al., “Artificial intelligence-based surveillance system for railway crossing traffic,” IEEE Sens. J., vol. 21, no. 14, pp. 15515–15526, Jul. 2021, doi: 10.1109/jsen.2020.3031861.

C. Sun et al., “MCA-YOLOV5-light: A faster, stronger and lighter algorithm for helmet-wearing detection,” Appl. Sci., vol. 12, no. 19, pp. 1-19, Oct. 2022, doi: 10.3390/app12199697.

X. Xu, X. Zhang, and T. Zhang, “Lite-YOLOv5: A lightweight deep learning detector for on-board ship detection in large-scene Sentinel-1 SAR images,” Remote Sens., vol. 14, no. 4, pp. 1–27, Feb. 2022, doi: 10.3390/rs14041018.

M. Ali et al., “RES: Real-time video stream analytics using edge enhanced clouds,” IEEE Trans. Cloud Comput., vol. 10, no. 2, pp. 792–804, Apr.–Jun. 2022, doi: 10.1109/TCC.2020.2991748.

X. Xia et al., “Cost-effective app data distribution in edge computing,” IEEE Trans. Parallel Distrib. Syst., vol. 32, no. 1, pp. 31–44, Jan. 2021, doi: 10.1109/TPDS.2020.3010521.

H. Alawad, S. Kaewunruen, and M. An, “Learning from accidents: Machine learning for safety at railway stations,” IEEE Access, vol. 8, pp. 633–648, Dec. 2020, doi: 10.1109/ACCESS.2019.2962072.

U. Nepal and H. Eslamiat, “Comparing YOLOv3, YOLOv4 and YOLOv5 for autonomous landing spot detection in faulty UAVs,” Sensors, vol. 22, no. 2, pp. 1–15, Jan. 2022, doi: 10.3390/s22020464.

P. Sikora, M. Kiac, and M.K. Dutta, “Classification of railway level crossing barrier and light signalling system using YOLOv3,” 2020 43rd Int. Conf. Telecommun. Signal Process. (TSP), 2020, pp. 528–532, doi: 10.1109/TSP49548.2020.9163535.

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” 2016 IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2016, pp. 779–788, doi: 10.1109/CVPR.2016.91.

J. Solawetz (2020) “What is Mean Average Precision (mAP) in Object Detection?,” [Online],, access date: 15-Jul-2023.

M. Veeramanikandan and S. Sankaranarayanan, “Publish/subscribe based multi-tier edge computational model in internet of things for latency reduction,” J. Parallel Distrib. Comput., vol. 127, pp. 18–27, May 2019, doi: 10.1016/j.jpdc.2019.01.004.

D.J. Shin and J.J. Kim, “A deep learning framework performance evaluation to use YOLO in Nvidia Jetson platform,” Appl. Sci., vol. 12, no. 8, pp. 1–19, Apr. 2022, doi: 10.3390/app12083734.

I. Resmadi, “Kajian moralitas teknologi pintu perlintasan kereta api (Studi kasus: Pintu perlintasan kereta api Cikudapateuh Bandung),” J. Sosioteknol. vol. 13, no. 2, pp. 84–90, Aug. 2014, doi: 10.5614/sostek.itbj.2014.13.2.2.

S. Valladares et al., “Performance evaluation of the Nvidia Jetson Nano through a real-time machine learning application,” Int. Conf. Intell. Hum. Syst. Integr., 2021, pp. 343–349, doi: 10.1007/978-3-030-68017-6_51.

A. Al-Qamash, I. Soliman, R. Abulibdeh, and M. Saleh, “Cloud, fog, and edge computing: A software engineering perspective,” 2018 Int. Conf. Comput. Appl. (ICCA), 2018, pp. 276–284, doi: 10.1109/COMAPP.2018.8460443.

How to Cite
Rian Putra Pratama, & Suhono Harso Supangkat. (2024). Application of Computation Offloading in Edge Computing-Based Level Crossing Violation Detection Systems. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, 13(1), 18-24.