A Novel Safe Life Extension Method for Aircraft Main Landing Gear Based on Statistical Inference of Test Life Data and Outfield Life Data

Fu, Yueshuai; Fu, Huimin; Zhang, Sheng

doi:10.3390/sym15040880

Cited by 2 publications

(1 citation statement)

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“…Several techniques, such as those described in references [11][12][13][14][15][16], strive to improve the precision of fatigue detection by identifying the same facial characteristics as described in reference [9]. To this end, Sigari et al [17] developed a method that compares the driver's present head orientation to a pre-existing facial template and projects the top half of the driver's facial image horizontally to detect alterations in eye closure and eyelid distance.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Fatigue Detection System Based on 3D CNNs and Ensemble Models

2023

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Due to the widespread issue of road accidents, researchers have been drawn to investigate strategies to prevent them. One major contributing factor to these accidents is driver fatigue resulting from exhaustion. Various approaches have been explored to address this issue, with machine and deep learning proving to be effective in processing images and videos to detect asymmetric signs of fatigue, such as yawning, facial characteristics, and eye closure. This study proposes a multistage system utilizing machine and deep learning techniques. The first stage is designed to detect asymmetric states, including tiredness and non-vigilance as well as yawning. The second stage is focused on detecting eye closure. The machine learning approach employs several algorithms, including Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Multi-layer Perceptron (MLP), Decision Tree (DT), Logistic Regression (LR), and Random Forest (RF). Meanwhile, the deep learning approach utilizes 2D and 3D Convolutional Neural Networks (CNNs). The architectures of proposed deep learning models are designed after several trials, and their parameters have been selected to achieve optimal performance. The effectiveness of the proposed methods is evaluated using video and image datasets, where the video dataset is classified into three states: alert, tired, and non-vigilant, while the image dataset is classified based on four facial symptoms, including open or closed eyes and yawning. A more robust system is achieved by combining the image and video datasets, resulting in multiple classes for detection. Simulation results demonstrate that the 3D CNN proposed in this study outperforms the other methods, with detection accuracies of 99 percent, 99 percent, and 98 percent for the image, video, and mixed datasets, respectively. Notably, this achievement surpasses the highest accuracy of 97 percent found in the literature, suggesting that the proposed methods for detecting drowsiness are indeed effective solutions.

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