Reference health
Comparative Analysis of AlexNet, ResNet18 and SqueezeNet with Diverse Modification and Arduous Implementation
29/29 checkable references clean · checked 2026-07-24
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
25 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 29 checked references that resolve
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Highway subsurface assessment using pavement surface distress and roughness data
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Approximating Empirical Surface Reflectance Data through Emulation: Opportunities for Synthetic Scene Generation
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Deep Transfer Learning for Vulnerable Road Users Detection using Smartphone Sensors Data
resolves10.1061/JPEODX.0000006
Three-Dimensional Pavement Crack Detection Algorithm Based on Two-Dimensional Empirical Mode Decomposition
resolves10.3390/app10030972
An Intelligent Classification Model for Surface Defects on Cement Concrete Bridges
resolves10.3390/rs11020152
An Efficient Framework for Remote Sensing Parallel Processing: Integrating the Artificial Bee Colony Algorithm and Multiagent Technology
resolves10.3390/s18103452
Automated Vision-Based Detection of Cracks on Concrete Surfaces Using a Deep Learning Technique
resolves10.1007/s00371-020-01868-8
Improved human action recognition approach based on two-stream convolutional neural network model
resolves10.1038/s41588-018-0241-6
Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps
resolves10.1109/IVS.2019.8814000
Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding
resolves10.3390/rs9080848
Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification
resolves10.1109/CIBCB.2018.8404980
A deep learning pipeline to classify different stages of Alzheimer's disease from fMRI data
resolves10.1109/CVPRW50498.2020.00190
Squeeze U-Net: A Memory and Energy Efficient Image Segmentation Network
resolves10.3390/s19050982
Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture
resolves10.1109/ICSPIS48872.2019.9066032
Document Image Classification using SqueezeNet Convolutional Neural Network
resolves10.1145/2800835.2800981
A smartphone based method to enhance road pavement anomaly detection by analyzing the driver behavior
resolves10.1007/978-981-10-3376-6_62
Erratum to: Progress in Intelligent Computing Techniques: Theory, Practice, and Applications
The 25 references without a DOI — listed, not checked
no DOI — not checkedSun Z.; Wang C.; Sha A.: Study of image-based pavement cracking measurement techniques,’ ICEMI 2009 - Proc. 9th Int. Conf. Electron. Meas. Instruments, pp. 2140–2143, 2009.,” [Online]. Available: Z. Sun, C. Wang, and A. Sha, “Study of image-based pavement cracking measurement techniques,” ICEMI 2009 - Proc. 9th Int. Conf. Electron. Meas. Instruments, pp. 2140–2143, (2009)
no DOI — not checkedMubaraki, M.: Study the Relationship between Pavement Surface Distress and Roughness Data,’ vol. 2012, pp. 4–8, 2016.,” [Online]. Available: M. Mubaraki, “Study the Relationship between Pavement Surface Distress and Roughness Data. 2012 4–8 (2016)
no DOI — not checkedSurampudi, R.; Koppula, B.; Rao, S.; Prasad, M.V.N.K.: First international conference on artificial intelligence and cognitive computing, 815: (2019)
no DOI — not checkedEdition, F.: ‘PAVEMENT SURFACE CONDITION RATING,’ (2016)
no DOI — not checkedFarashah, M.K.: Development practices for municipal pavement management systems application. (2012)
no DOI — not checkedPuan, O.C.; Mustaffar, M.; Ling, T.-C.: AUTOMATED PAVEMENT IMAGING PROGRAM (APIP) FOR PAVEMENT CRACKS CLASSIFICATION AND QUANTIFICATION,’ (2007)
no DOI — not checkedBerkeley, U.C.; Davis, U.C.: 100-epoch ImageNet Training with AlexNet in 24 Minutes. (2017)
no DOI — not checkedVaswani, A., et al.: Attention is all you need,” Adv. Neural Inf. Process. Syst., vol. 2017-December, no. Nips, pp. 5999–6009. (2017)
no DOI — not checkedDevlin, J.; Chang, M.W.; Lee, K.; Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. NAACL HLT 2019 - 2019 Conf. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol. - Proc. Conf., 1, pp. 4171–4186, (2019)
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no DOI — not checkedLocatello, F., et al.(2019) Challenging common assumptions in the unsupervised learning of disentangled representations. 36th Int. Conf. Mach. Learn. ICML 2019: 2019: 7247–7283 (2019)
no DOI — not checkedTouvron, H.; Cord, M.; Douze, M.; Massa, F.; Sablayrolles, A.; Jégou, H.: Training data-efficient image transformers & distillation through attention. (2020), [Online]. Available: http://arxiv.org/abs/2012.12877.
no DOI — not checkedFan, Z.; Member, S.; Wu, Y.; Lu, J.; Li, W.: Based on structured prediction with the convolutional neural network. pp. 1–9
no DOI — not checkedShijie, J.; Ping, W.: Research on data augmentation for image classification based on convolution neural networks. no. 201602118
no DOI — not checkedJean, G.; Banon, F.: Mathematical morphology and its applications to signal and image processing. (2007)
no DOI — not checkedhttps://data.mendeley.com/datasets/xnzhj3x8v4/2,” [Online]. Available: https://data.mendeley.com/datasets/xnzhj3x8v4/2.
no DOI — not checked“https://github.com/datasets,” [Online]. Available: https://github.com/datasets.
no DOI — not checkedhttps://github.com/google-research-datasets/Objectron,” [Online]. Available: https://github.com/google-research-datasets/Objectron.
no DOI — not checkedhttps://data.mendeley.com/datasets/5y9wdsg2zt/2,” [Online]. Available: https://data.mendeley.com/datasets/5y9wdsg2zt/2.
no DOI — not checkedhttps://www.kaggle.com/sachinpatel21/pothole-image-dataset,” [Online]. Available: https://www.kaggle.com/sachinpatel21/pothole-image-dataset.
no DOI — not checkedhttps://digitalcommons.usu.edu/all_datasets/48/,” [Online]. Available: https://digitalcommons.usu.edu/all_datasets/48/.
no DOI — not checkedAlom Z.; Taha T.M.; Yakopcic, C.S.; Westberg; Sidike, P.; Nasrin, M.S.: The history began from AlexNet: a comprehensive survey on deep learning approaches. (2018).
no DOI — not checkedKrizhevsky, B.A.; Sutskever, I. Hinton G.E.: ImageNet classification with deep convolutional neural networks. (2012)
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no DOI — not checkedJin, P.; Adu-Gyamfi Professor, Y.; Buttlar Professor, W.G.; Barton Chair, G.: PID: A new benchmark dataset to classify and densify pavement distresses hamed majidifard, Corresponding Author,” no. October, (2018).
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