The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD
Open accessAbstract
This paper aims to achieve real-time and accurate detection of surface defects by using a deep learning method. For this purpose, the Single Shot MultiBox Detector (SSD) network was adopted as the meta structure and combined with the base convolution neural network (CNN) MobileNet into the MobileNet-SSD. Then, a detection method for surface defects was proposed based on the MobileNet-SSD. Specifically, the structure of the SSD was optimized without sacrificing its accuracy, and the network structure and parameters were adjusted to streamline the detection model. The proposed method was applied to the detection of typical defects like breaches, dents, burrs and abrasions on the sealing surface of a container in the filling line. The results show that our method can automatically detect surface defects more accurately and rapidly than lightweight network methods and traditional machine learning methods. The research results shed new light on defect detection in actual industrial scenarios.
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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
(2016) · Cited by 31,590
SSD: Single Shot MultiBox Detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_2 · Cited by 22,128
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks
Zhang, K., Zhang, Z., Li, Z., & Qiao, Y. (2016) · IEEE Signal Processing Letters · doi:10.1109/lsp.2016.2603342 · Cited by 6,214
Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks
Cha, Y., Choi, W., & Büyüköztürk, O. (2017) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12263 · Cited by 3,295
Robust human activity recognition from depth video using spatiotemporal multi-fused features
Jalal, A., Kim, Y.-H., Kim, Y.-J., Kamal, S., & Kim, D. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2016.08.003 · Cited by 384
Wheel Defect Detection With Machine Learning
Krummenacher, G., Ong, C. S., Koller, S., Kobayashi, S., & Buhmann, J. M. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2720721 · Cited by 258
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Cited by
Recent advances in surface defect inspection of industrial products using deep learning techniques
Zheng, X., Zheng, S., Kong, Y., & Chen, J. (2021) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-021-06592-8 · Cited by 287
Li, Y., Han, Z., Xu, H., Liu, L., Li, X., & Zhang, K. (2019) · Applied Sciences · doi:10.3390/app9183781 · Cited by 115
VITO-Transformer: A Visual-Tactile Fusion Network for Object Recognition
Li, B., Bai, J., Qiu, S., Wang, H., & Guo, Y. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3326241 · Cited by 39
Wang, B., Wang, M., Yang, J., & Luo, H. (2023) · Measurement: Sensors · doi:10.1016/j.measen.2023.100909 · Cited by 27
Metadata
- Authors
- Li, Y., Huang, H., Xie, Q., Yao, L., & Chen, Q.
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2018-09-17
- Volume
- 8
- Issue
- 9
- Pages
- 1678
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W2891336752
- Cited by
- 322
- References
- 26
- FWCI
- 21.81
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
