AI-Based Tire Pressure Detection Using an Enhanced Deep Learning Architecture
Source abstract
Abstract
Tires are integral to vehicular systems, directly influencing both safety and overall performance. Traditional tire pressure inspection methods—such as manual or gauge-based approaches—are often time-consuming, prone to i... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Vehicle Dynamics and Control Systems · Vehicle emissions and performance · Autonomous Vehicle Technology and Safety
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (37)
Show source-provided references
1. Machine vision intelligence for product defect inspection based on deep learning and Hough transform10.1016/j.jmsy.2019.03.002 ↗
2. State of the art in defect detection based on machine vision10.1007/s40684-021-00343-6 ↗
3. Detection of vehicle position and speed using camera calibration and image projection methods10.1016/j.procs.2019.08.165 ↗
4. PCA-based feature reduction for hyperspectral remote sensing image classification10.1080/02564602.2020.1740615 ↗
5. The modulo radon transform: theory, algorithms, and applications10.1137/21M1424615 ↗
6. Dimensionality reduction by collaborative preserving Fisher discriminant analysis10.1016/j.neucom.2019.05.014 ↗
7. Independent component analysis: algorithms and applications10.1016/S0893-6080(00)00026-5 ↗
8. Independent component analysis: an introduction10.1016/j.aci.2018.08.006 ↗
9. Adaptive dimensionality reduction for neural network-based online principal component analysis10.1371/journal.pone.0248896 ↗
10. A review of image denoising algorithms, with a new one10.1137/040616024 ↗
11. Image fusion with guided filtering10.1109/TIP.2013.2244222 ↗
12. Image reconstruction algorithms for electrical capacitance tomography10.1088/0957-0233/14/1/201 ↗
13. Image processing with neural networks—a review10.1016/S0031-3203(01)00178-9 ↗
14. Region of interest segmentation based on clustering techniques for breast cancer ultrasound images: a review
15. Reverberation noise suppression in ultrasound channel signals using a 3D fully convolutional neural network10.1109/TMI.2021.3049307 ↗
16. The upsurge of deep learning for computer vision applications
17. Gluoncv and gluonnlp: deep learning in computer vision and natural language processing
18. Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey10.1007/s10462-018-09679-z ↗
19. A survey of deep learning and its applications: a new paradigm to machine learning10.1007/s11831-019-09344-w ↗
20. Fast sparse coding networks for anomaly detection in videos10.1016/j.patcog.2020.107515 ↗
21. Video anomaly detection with sparse coding inspired deep neural networks10.1109/TPAMI.2019.2944377 ↗
22. A deep learning-based approach for mitigating falls from height with computer vision: convolutional neural network10.1016/j.aei.2018.12.005 ↗
23. Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions10.1038/s41467-019-12875-2 ↗
24. Transfer learning using VGG-16 with deep convolutional neural network for classifying images
25. Comparative analysis of image classification algorithms based on traditional machine learning and deep learning10.1016/j.patrec.2020.07.042 ↗
26. Deep support vector machine for hyperspectral image classification10.1016/j.patcog.2020.107298 ↗
27. Genetic algorithm: theory, literature review, and application in image reconstruction
28. Intelligent vehicle network system and smart city management based on genetic algorithms and image perception10.1016/j.ymssp.2020.106623 ↗
29. A novel image steganographic method based on integer wavelet transformation and particle swarm optimization10.1016/j.asoc.2020.106257 ↗
30. Color image encryption using non-dominated sorting genetic algorithm with local chaotic search based 5D chaotic map10.1016/j.future.2020.02.029 ↗
31. Application of automation for in-line quality inspection, a zero-defect manufacturing approach10.1016/j.jmsy.2022.12.010 ↗
32. Brain image segmentation based on FCM clustering algorithm and rough set10.1109/ACCESS.2019.2893063 ↗
33. Research on tire crack detection using image deep learning method10.1038/s41598-023-35227-z ↗
34. Going deeper with convolutions
35. Rethinking the inception architecture for computer vision
36. Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size. arXiv:1602.07360. 2016.
37. Compressed residual-VGG16 CNN model for big data places image recognition