FPGA Based Neural Processor Design For Image Detection of Traffic Signs

With the increasing complexity of traffic management systems, there is a critical need for efficient, high-performance solutions capable of processing visual data in real time to improve traffic control and safety. Traditional processors often face computational bottlenecks and latency issues when deploying neural networks for image recognition tasks. To address these limitations, this work proposes a dedicated neural processor implemented on the Cyclone IV FPGA, optimized for traffic signal image processing. The design leverages the parallelism and reconfigurability of FPGAs, enabling significant acceleration of computations while maintaining low power consumption. At its core, the processor integrates a neural network architecture with 4 layers tailored for detecting and classifying traffic signal colors, such as red, yellow, and green. This neural network is implemented using VHDL, ensuring efficient use of FPGA resources while achieving high computational performance. Key features of the design include real-time data processing, a focus on speed and accuracy, and a compact implementation that balances resource utilization. The effectiveness of the FPGA-based neural processor is evaluated using real-world traffic signal image datasets, demonstrating robust performance with 92% classification accuracy. Comprehensive evaluations of processing speed, classification accuracy, and resource utilization affirm the efficacy of the proposed neural network-based FPGA solution in addressing the complexities of real-time traffic signal processing and modern traffic management challenges.