Wood Surface Defects Detection Based on AI Algorithm Yolov7
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-54288-6_2
Reference7 articles.
1. Kodytek, P., Bodzas, A., Bilík, P.: A large-scale image dataset of wood surface defects for automated vision-based quality control processes. F1000Research 10, 581 (2022). https://doi.org/10.12688/f1000research.52903.2
2. Kodytek, P., Bodzas, A., Bilík, P.: Supporting data for deep learning and machine vision based approaches for automated wood defect detection and quality control. Zenodo (2021). https://doi.org/10.5281/zenodo.4694695
3. MacEachern, C.B., Esau, T., Schumann, A.W., Hennessy, P., Zaman, Q.: Detection of fruit maturity stage and yield estimation in wild blueberry using deep learning convolutional neural networks. Smart Agric. Technol. 3, 100099 (2023). https://doi.org/10.1016/j.atech.2022.100099
4. Zaccaria, M., Monica, R., Aleotti, J.: A comparison of deep learning models for pallet detection in industrial warehouses. In: IEEE Conference Publication. IEEE Xplore, 3 September 2020. https://ieeexplore.ieee.org/document/9266168
5. Císař, P., Bekkozhayeva, D., Movchan, O., Saberioon, M., Schraml, R.: Computer vision based individual fish identification using skin dot pattern. Sci. Rep. 11(1) (2021). https://doi.org/10.1038/s41598-021-96476-4
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