Deep learning for 6D pose estimation of objects — A case study for autonomous driving

Hoque, Sabera; Xu, Shuxiang; Maiti, Ananda; Wei, Yuchen; Arafat, Md. Yasir

doi:10.1016/j.eswa.2023.119838

Cited by 17 publications

(1 citation statement)

References 25 publications

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“…Deep learning [6] has achieved impressive performance in visual domains such as autonomous driving [7,8] and healthcare [9,10], creating new opportunities for experts to utilize images directly in their AI applications. Unlike traditional complex theoretical methods, deep learning fits a large number of parameters through mimicking the human learning process.…”

Section: Introductionmentioning

confidence: 99%

Training Tricks for Steel Microstructure Segmentation with Deep Learning

Ma,

2023

Processes

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Data augmentation and other training techniques have improved the performance of deep learning segmentation methods for steel materials. However, these methods often depend on the dataset and do not provide general principles for segmenting different microstructural morphologies. In this work, we collected 64 granular carbide images (2048 × 1536 pixels) and 26 blocky ferrite images (2560 × 1756 pixels). We used five carbide images and two ferrite images and derived from them the test set to investigate the influence of frequently used training techniques on model segmentation accuracy. We propose a novel method for quickly building models that achieve the highest segmentation accuracy for a given dataset through combining multiple training techniques that enhance the segmentation quality. This method leads to a 1–2.5% increase in mIoU values. We applied the optimal models to the quantization of carbides. The results show that the optimal models achieve the smallest errors of 5.39 nm for the mean radius and 29 for the total number of carbides on the test set. The segmentation results are also more reasonable than those of traditional segmentation methods.

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