Real-Time Object Detection and Recognition in FPGA-Based Autonomous Driving Systems |
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| © 2024 by IJCTT Journal | ||
| Volume-72 Issue-4 |
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| Year of Publication : 2024 | ||
| Authors : Muthukumaran Vaithianathan | ||
| DOI : 10.14445/22312803/IJCTT-V72I4P119 | ||
How to Cite?
Muthukumaran Vaithianathan, "Real-Time Object Detection and Recognition in FPGA-Based Autonomous Driving Systems," International Journal of Computer Trends and Technology, vol. 72, no. 4, pp. 145-152, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I4P119
Abstract
This research paper presents an innovative methodology for the identification and detection of objects in autonomous driving systems that employ field-programmable gate arrays (FPGAs). Through the integration of deep learning methodologies with FPGA hardware acceleration, the approach successfully attains the minimal latency and optimal precision necessary for secure navigation. By conducting data acquisition, preprocessing, and model training, this can refine the system's performance. By employing parallel computing and hardware optimisation techniques, the FPGA implementation achieves these objectives. Based on experimental data, the FPGA-based approach outperforms conventional CPU and GPU implementations in terms of power efficiency, inference latency, and detection precision. The widespread adoption of field-programmable gate arrays (FPGAs) for enhanced object recognition and identification in autonomous vehicles is imminent due to their exceptional compatibility with autonomous driving systems.
Keywords
Real-Time Object Detection, Object Recognition, Field Programmable Gate Array, Deep Learning, Autonomous Driving.
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