Real-time uncertainty estimation of stripe center extraction results using adaptive BP neural network. (15th May 2022)

• Record Type: Journal Article
• Authors: Zhou, Jingbo; Wang, Kai; Yang, Guang; Liu, Xiaohong; Du, Renjie; Li, Yuehua
• Is Part Of: Measurement. Volume 194(2022)
• Publication Date: 2022-05-15
• Subjects: Machine vision -- Center extraction -- Uncertainty estimation -- Adaptive BP neural network; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.111022
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: An adaptive BP neural network is proposed for real time uncertainty computation of laser stripes. Network design, computation and training methods are described in detail. Uncertainty values from the adaptive BP neural network well conform to the results of Monte Carlo method. A comparison of uncertainty results between classical center extraction methods is carried out. Abstract: Center extraction of stripe images is a key technique of line structured light sensors (LSLSs). As the original data for profile computation, the extraction results, which are highly affected by image quality, should be accurate and reliable. While, few works can be found for real-time uncertainty evaluation of the extraction results due to the diversity of center extraction methods and the complexity of uncertainty models. Here, a universal method is proposed to evaluate the uncertainty of center extraction results obtained from classical methods including, but not limited to, the gray gravity method (GGM), Gaussian fitting method (GFM) and Steger method. The proposed method is based on an adaptive BP neural network (ABPNN) where its weights and thresholds are adjusted automatically according to the width of each cross section profile. Experimental results show that the ABPNN can predict the uncertainty value of center extraction results accurately and efficiently.