Straight-Line Detection within 1 Millisecond per Frame for Ultra-High-Speed Industrial Automation

Songlin Du, Ziwei Dong, Yuan Li, Takeshi Ikenaga

Research output: Contribution to journalArticlepeer-review

Abstract

Detecting straight lines in video plays a fundamental role in camera-based industrial automation. With the increasing demands on production efficiency, detection speed becomes one of the bottlenecks for highly-efficient industrial automation. Because of data dependency and hardware limitation, existing vision systems based on CPU/GPU are unable to detect straight lines at ultra-high speed. This paper addresses this problem and proposes a hardware-friendly Hough transform that can be implemented in fully parallel for ultra-high-speed detection, because of two key features: 1) It processes multiple pixels in parallel and directly calculates line parameters while capturing the current frame; 2) it simultaneously initializes Hough parameter space and votes in Hough parameter space without any delay. Based on the proposed hardware-friendly Hough transform, its chip-level implementation and system-level hardware design are presented. Experimental results show that the main benefits of the proposed architecture are in real-time performances of high frame rate (784 FPS) and ultra-low delay (0.7749 ms/frame).

Original languageEnglish
JournalIEEE Transactions on Industrial Informatics
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Automation
  • Delays
  • Field programmable gate arrays
  • Hardware
  • High frame rate
  • Image edge detection
  • parallel Hough transform
  • Standards
  • straight-line detection
  • Transforms
  • ultra-low delay

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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