This thesis presents a new hardware accelerated approach using image processing and detection algorithms for implementing fast and robust traffic sign detection system with focus on speed limit sign detection. The proposed system targets reconfigurable integrated circuits particularly Field Programmable Gate Array (FPGA) devices. This work propose a fully parallelized and pipelined parallel system architecture to exploit the high performance and flexibility capabilities of FPGA devices. This thesis is divided into two phases, the first phase, is a software prototype implementation of the proposed system. The software system was designed and developed using C++ and OpenCV library on general purpose CPU. The prototype is used to explore and investigate potential segmentation and detection algorithms that might be feasible to design and implement in hardware accelerated environments. These algorithms includes RGB colour conversion, colour segmentation through thresholding, noise reduction through median filter, morphological operations through erosion and dilation, and sign detection through template matching. The second phase, a hardware-based design of the system was developed using the same algorithms used in the software design. The hardware design is composed of 20 image processing components each designed to xxx fully parallelized and pipelined xxx. The hardware implementation was developed using VHDL as the hardware description language targeting a Xilinix Virtex-6 FPGA XC6VLX240T device. The development environment is Xilinx ISE®Design Suite version 14.3. A set of 20 640x480 test images was used as the test data for the verification and testing of this work. The images was captured by a smart-phone camera in various weather and lightning conditions. The software implementation delivered speed limit detection results with a success rate of 75%. The hardware implementation was only simulated using Xilinx ISE Simulator (ISim) with a overall system latency of 12964 clock cycles. According to the Place and Route report the maximum operation frequency for the proposed hardware design is 71,2 MHz. The design only utilized 2% of the slice registers, 4% of the slice Look up Tables (LUT), and 11% of the block memory. This thesis project concludes the work based on the provided software and hardware implementation and performance analysis results. Also the conclusions chapter provides recommendations and future work for possible extension of the project.

This thesis presents a new hardware accelerated approach using image processing and detection algorithms for implementing fast and robust traffic sign detection system with focus on speed limit sign detection. The proposed system targets reconfigurable integrated circuits particularly Field Programmable Gate Array (FPGA) devices. This work propose a fully parallelized and pipelined parallel system architecture to exploit the high performance and flexibility capabilities of FPGA devices. This thesis is divided into two phases, the first phase, is a software prototype implementation of the proposed system. The software system was designed and developed using C++ and OpenCV library on general purpose CPU. The prototype is used to explore and investigate potential segmentation and detection algorithms that might be feasible to design and implement in hardware accelerated environments. These algorithms includes RGB colour conversion, colour segmentation through thresholding, noise reduction through median filter, morphological operations through erosion and dilation, and sign detection through template matching. The second phase, a hardware-based design of the system was developed using the same algorithms used in the software design. The hardware design is composed of 20 image processing components each designed to xxx fully parallelized and pipelined xxx. The hardware implementation was developed using VHDL as the hardware description language targeting a Xilinix Virtex-6 FPGA XC6VLX240T device. The development environment is Xilinx ISE®Design Suite version 14.3. A set of 20 640x480 test images was used as the test data for the verification and testing of this work. The images was captured by a smart-phone camera in various weather and lightning conditions. The software implementation delivered speed limit detection results with a success rate of 75%. The hardware implementation was only simulated using Xilinx ISE Simulator (ISim) with a overall system latency of 12964 clock cycles. According to the Place and Route report the maximum operation frequency for the proposed hardware design is 71,2 MHz. The design only utilized 2% of the slice registers, 4% of the slice Look up Tables (LUT), and 11% of the block memory. This thesis project concludes the work based on the provided software and hardware implementation and performance analysis results. Also the conclusions chapter provides recommendations and future work for possible extension of the project.