Evaluation of data augmentation and loss functions in semantic image segmentation for drilling tool wear detection
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Published:2024-02-08
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Volume:
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ISSN:0956-5515
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Container-title:Journal of Intelligent Manufacturing
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language:en
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Short-container-title:J Intell Manuf
Author:
Schlager ElkeORCID, Windisch Andreas, Hanna Lukas, Klünsner Thomas, Hagendorfer Elias Jan, Feil Tamara
Funder
Österreichische Forschungsförderungsgesellschaft
Publisher
Springer Science and Business Media LLC
Reference47 articles.
1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., ..., & Zheng, X. (2015). TensorFlow: Large-scale machine learning on heterogeneous systems. https://www.tensorflow.org/. Software available from tensorflow.org. 2. Abulnaga, S. M., & Rubin, J. (2019). Ischemic stroke lesion segmentation in CT perfusion scans using pyramid pooling and focal loss. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part I 4, pp. 352–363. Springer. 3. Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481–2495. https://doi.org/10.1109/TPAMI.2016.2644615 4. Bai, H., Cheng, J., Su, Y., Liu, S., & Liu, X. (2022). Calibrated focal loss for semantic labeling of high-resolution remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 6531–6547. https://doi.org/10.1109/JSTARS.2022.3197937 5. Bergs, T., Holst, C., Gupta, P., & Augspurger, T. (2020). Digital image processing with deep learning for automated cutting tool wear detection. Procedia Manufacturing, 48, 947–958. https://doi.org/10.1016/j.promfg.2020.05.134
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