Design and Implementation of FPGA Accelerator for Vision Based Fire Detection

Abstract

Fire outbreaks are great threat to human beings, economic infrastructure, and the environment. This led to the need for timely and accurate detection of fire incidents to minimize their devastating impacts. But conventional fire detection systems are electronic sensor based which as result they suffer from the problems of transport delay, conduction delay, limited detection range, high false alarm, and inappropriate for outdoor applications. To address the shortcomings of such sensor-based methods, several image processing and computer vision approaches to fire detection have been proposed. However, these image processing and computer vision-based solutions are implemented in software platforms which have disadvantages of inefficiency, high hardware requirements and high cost. In this research work, an embedded hardware accelerator for vision-based fire detection was designed and implemented in Kintex-7 series Field Programmable Gate Array (FPGA). MATLAB R2021a software was used for decoding the image dataset into pixel stream data. The design was captured in very high-speed integrated circuit HDL (VHDL). The design was synthesized with Xilinx Vivado 2021 design suite and simulated with Xilinx ISIM. Evaluation results showed that the accelerator could achieve good detection of fire features and were consistent with the results from MATLAB code running on personal computer. Compared to software implementation, the latency, resource utilization and power consumption was greatly reduced. It was also found that the hardware accelerator which was developed as an Intellectual Property (IP) core can also be employed to speed up grayscale conversion, edge detection and thresholding algorithms in embedded vision, smart camera and video analytics applications.