由於直線偵測演算法需要一連串循序、復雜的影像處理流程，因此在軟體實現的時候需要利用高效能的處理器來降低計算的處理時間。本論文利用MIAT(本實驗室)系統方法論，設計一個平行化運算的直線偵測硬體加速器。此加速器架構包含一個上層的管線化控制器，用來控制底下Canny邊緣偵測模組以及Hough Transform模組的運作，利用邊緣偵測模組得到的影像邊緣資訊進行Hough Transform以提高直線偵測結果的準確度，再利用管線化控制的方式來提升各模組的效能。本研究根據Canny的流程使用高斯模糊處理降低雜訊造成的錯誤邊緣偵測，並且在不影響直線偵測結果的前提下，減少運算次數及資源使用量，用以提升系統效能，相較於Xu與Chen的方式分別降低了84％與74％的電路資源使用。利用方法論生成之硬體功能電路，具備好的分散式架構以及可擴充性，使其更容易應用在各式嵌入式系統中。

Due to line detection algorithm require a series of sequential complex image processing. Therefore, implementing line detection algorithm with software usually needs high-efficiency processor to reduce the processing time. In this paper, MIAT system design methodology is used to design a parallel calculating line detection hardware accelerators. Architecture of the accelerator contains a top layer pipeline controller to control Canny edge detection module and Hough Transform module below. Hough Transform module use the information from edge detection module to enhance accuracy. And pipeline control is used to improve efficacy of each module. According to Canny’s method we used Gaussian blur processing to reduce wrong edge detection cause by noise. In the case does not affect the line detection result we reducing the number of operations and the amount of resources to Enhance system performance. Compared to Chen’s research and Xu’ research our method reduce 84％ and 74％ circuit resource. Hardware circuits generate by methodology, with good distributed architecture and scalability, making it easier to use in a variety of embedded systems.

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