FAST Algorithm for Accurate Corner Points Detection of Section Steel Based on Adaptive Threshold

doi:10.16183/j.cnki.jsjtu.2023.276

Abstract

Abstract:

The on-line flatness detection of section steel based on machine vision is a key technical problem for quickly and accurately extracting key corner points from section steel images to enable accurate detection. Aiming at the problem that the features from accelerated segment test (FAST) algorithm needs to manually set the corner points screening threshold and there are numerous false corner points in corner point extraction, this paper proposes an adaptive threshold generation and correction strategy. Based on the automatic determination of the initial threshold, this strategy can adjust the threshold in real time until an appropriate value is reached according to the requirements of the initial corner points set, thereby to reduce the risk of missing key corner points. In addition to using FAST algorithm to extract corner points, the smallest univalue segment assimilating nucleus (SUSAN) algorithm is employed to eliminate false corner points ensuring the effectiveness of key corner points extraction. The experiments prove that the FAST corner detection algorithm based on adaptive threshold (FAST-A) can still accurately and quickly detect key corner points even when the detection environment and object characteristics change. Furthermore, the algorithm proposed provides accurate corner points for real-time section steel flatness detection, and improves the adaptability of corner points extraction.

Key words: section steel, corner points detection, features from accelerated segment test (FAST) algorithm, smallest univalue segment assimilating nucleus (SUSAN) algorithm, adaptive threshold

CLC Number:

TP391.4

BAO Jiahan, SUN Deshang, HUANG Jianzhong, HU Zheng. FAST Algorithm for Accurate Corner Points Detection of Section Steel Based on Adaptive Threshold[J]. Journal of Shanghai Jiao Tong University, 2025, 59(5): 691-702.

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阈值变化	M_n_-1变化	状态判断
增大	减少	正常
减少	增大	正常
增大	增大	异常
减少	减少	异常
增大或减少	M≫b	异常