Fast and Resource-Efficient Hardware Implementation of Modified Line Segment Detector

Abstract

Lines are significant features enclosing high-level information in an image. The line segment Detector (LSD) Algorithm with low error rate is a widely used method to extract lines in images effectively and accurately. However, the algorithm on PC performs too costly both in time and resources for the real-time video processing. This paper provides a fast and resource-efficient hardware implementation solution for a modified LSD algorithm on Field Programmable Gate Arrays (FPGA) for real-time line detection. The task-level pipeline structures are exploited fully in a stream process mapped to the hardware architecture free of frame buffer. Our proposed hardware implementation processes in a stream-in–stream-out manner with little consumption of the on-chip block RAM to store intermediate values. We first employ hardware Gaussian filter and adjust Canny edge detection to obtain an edge map at single-pixel width. Then, a novel structure of region growing model based on dynamic rooted tree is used to detect line segment regions accurately with a latency of only a few rows of pixels. The low cost in time, on-chip resources, and power consumption makes our proposed algorithm suitable for portable real-time streaming video processing applications using line segment features, such as Lane departure warning systems. It can also be applied in real-time machine vision systems that use line segments information for further recognition or stereo correspondence and many others. The proposed algorithm is synthesized and tested on XC7Z020 FPGA with high reliability, accuracy speed, and low cost in both resources and energy.