Deep Learning Object Detection Techniques for Thin Objects in Computer Vision: An Experimental Investigation

Jaffari, Rabeea; Hashmani, Manzoor Ahmed; Reyes-Aldasoro, Constantino Carlos; Aziz, Norshakirah; Rizvi, Syed Sajjad Hussain

doi:10.1109/iccar52225.2021.9463487

Cited by 9 publications

(1 citation statement)

References 34 publications

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“…Moreover, apart from ML, a few [ 27 , 28 ] studies have utilized deep learning (DL) techniques on AE signal data to localize AE sources in common and complex metallic panels, with autoencoders used in [ 27 ] and for the detection and localization of cracks in steel rails under loads in [ 28 ]. However, DL techniques are often limited in their function due to their reliance on large training datasets, as discussed by the researchers in [ 29 ]. Hence, inspired by advancements in ML and AE signal processing and analysis, this work was undertaken to fill the existing research gaps by proposing an ML-based classification approach for coating disbondment failure severity prediction via AE features.…”

Section: Introductionmentioning

confidence: 99%

Failure Severity Prediction for Protective-Coating Disbondment via the Classification of Acoustic Emission Signals

Rahman

May

Jaffari

et al. 2023

Sensors

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Structural health monitoring is a popular inspection method that utilizes acoustic emission (AE) signals for fault detection in engineering infrastructures. Diagnosis based on the propagation of AE signals along any surface material offers an attractive solution for fault identification. However, the classification of AE signals originating from failure events, especially coating failure (coating disbondment), is a challenging task given the AE signature of each material. Thus, different experimental settings and analyses of AE signals are required to classify the various types of coating failures, and they are time-consuming and expensive. Hence, to address these issues, we utilized machine learning (ML) classification models in this work to evaluate epoxy-based-protective-coating disbondment based on the AE principle. A coating disbondment experiment consisting of coated carbon steel test panels for the collection of AE signals was implemented. The obtained AE signals were then processed to construct the final dataset to train various state-of-the-art ML classification models to divide the failure severity of coating disbondment into three classes. Consequently, methods for the extraction of useful features, the handling of data imbalance, and a reduction in the bias of ML models were also effectively utilized in this study. Evaluations of state-of-the-art ML classification models on the AE signal dataset in terms of standard metrics revealed that the decision forest classification model outperformed the other state-of-the-art models, with accuracy, precision, recall, and F1 score values of 99.48%, 98.76%, 97.58%, and 98.17%, respectively. These results demonstrate the effectiveness of utilizing ML classification models for the failure severity prediction of protective-coating defects via AE signals.

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