The Implement of Vehicle Blind Spot Detection System Using SDSoC


Chayanin Youngyai Wannarat Suntiamorntut


        Embedded system and image processing is the technology used to apply for designing systems to facilitate drivers in the future by replacing wing mirrors with the camera to detect objects at the blind spot at the back. When the object is detected within the blind spot zone, the system is alarm drivers. These are a concept of The blind spot detection vehicle system (BSDS). This research presents BSDS to help drivers, which is designed on Field Programmable Gate Array (FPGA). There are two main modules for creating. The first module is pre-processing image using grayscale to resize the dataset in terms of the bit stream in every pixel and region-of-interest (ROI) for specifying the area of pixels wanted to reduce the noise of the data set or data quantity that needs to use in calculation. The second module is vehicle detection and alarm by Sobel operator for object and shadow detection to detect the shadow of the vehicle and confirm the existence of it. This research shows details of each algorithm, flow chart, and effectiveness of vehicle detection. This idea has been designed and developed by a software called Software-Defined System-On-Chip (SDSoC) on the Zybo board. In conclusion, the system can detect vehicles at resolution 1920*1080 within 12 ms/frame and accuracy of vehicle detection at 100% recall.

Keywords: SDSoC, FPGA, BSDS, image-processing, pre-processing image, region-of-interest, Sobel operator, shadow detection


Anusha, G., JayaChandra, P., & Satya, N. (2012). Implementation of Sobel Edge Detection on FPGA. International Journal of Computer Trends and Technology, 3(3), 472-475. Retrieved from http://
Ajay, T. S., & Ezhil, R. (2016). Detecting Blind Spot by Using Ultrasonic Sensor. International Journal of Scientific & Technology Research, 5(5), 195-196. Retrieved from
Baek, J. W., Lee, E., Park, M. R., & Seo, D. W. (2015). Mono-camera based side vehicle detection for blind spot detection systems. 2015 Seventh International Conference on Ubiquitous and Future Networks, 7-10 July 2015 (pp. 147-149). Sapporo, Japan: Institute of Electrical and Electronics Engineers.
Cayir, B., & Acarman, T. (2009). Low cost driver monitoring and warning system development. 2009 IEEE Intelligent Vehicles Symposium, 3-5 June 2009 (pp. 94–98). Xi'an, China: Institute of Electrical and Electronics Engineers.
Chang, W. C., & Hsu, K. J. (2010). Vision-based side vehicle detection from a moving vehicle. 2010 International Conference on System Science and Engineering, 1-3 July 2010 (pp. 553-558). Taipei, Taiwan: Institute of Electrical and Electronics Engineers.
Chen, C. T., & Chen, Y. S. (2009). Real-time approaching vehicle detection in blind-spot area. Proceedings of the 12th International IEEE Conference on Intelligent Transportation Systems (ITSC ’09), 4-7 October 2009 (pp. 24–29). St. Louis, MO, USA: Institute of Electrical and Electronics Engineers.
Fernández, C., Llorca, D. F., Sotelo, M. A., Daza, I. G., Hellín, A. M., & Álvarez, S. (2013). Real-time vision-based blind spot warning system: Experiments with motorcycles in daytime/nighttime conditions. International Journal of Automotive Technology, 14(1), 113–122.
Jung, K. H., & Yi, K. (2018). Vision-Based Blind Spot Monitoring Using Rear-View Camera and Its Real-Time Implementation in an Embedded System. Journal of Computing Science and Engineering, 12(3), 127-138.
Nong, M. A. M., Osman, R., Yusof, J. M., & Sidek, R. (2015). Real time motorcycle image detections on field programmable gate array. 2015 IEEE Regional Symposium on Micro and Nanoelectronics (RSM), 19-21 August 2015 (pp. 1-4). Kuala Terengganu, Malaysia: Institute of Electrical and Electronics Engineers.
Poonam, A. K., & Gajanan, P. D. (2017). Image Processing Based Vehicle Detection and Tracking System. Retrieved from
Song, K. T., Chen, C. H., & Chiu Huang, C. H. (2004). Design and Experimental Study of an Ultrasonic Sensor System for Lateral Collision Avoidance at Low Speeds. IEEE Intelligent Vehicles Symposium, 14-17 June 2004 (pp. 647-652). Parma, Italy: Institute of Electrical and Electronics Engineers.
Sotelo, M. A., & Barriga, J. (2008). Blind spot detection using vision for automotive applications. Journal of Zhejiang University SCIENCE A, 9(10), 1369-1372.

Research Articles


How to Cite
YOUNGYAI, Chayanin; SUNTIAMORNTUT, Wannarat. The Implement of Vehicle Blind Spot Detection System Using SDSoC. Naresuan University Journal: Science and Technology (NUJST), [S.l.], v. 28, n. 3, p. 96-109, june 2020. ISSN 2539-553X. Available at: <>. Date accessed: 22 jan. 2022. doi: