References
- Shin, S., Han, H., & Lee, S. H. (2021). Improved YOLOv3 with duplex FPN for object detection based on deep learning. The International Journal of Electrical Engineering & Education, 002072092098352. https://doi.org/10.1177/0020720920983524
- Kirillov, A., He, K., Girshick, R., Rother, C., & Dollar, P. (2019). Panoptic Segmentation. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019-June, 9396-9405. https://doi.org/10.1109/CVPR.2019.00963
- Shelhamer, E., Long, J., & Darrell, T. (2017). Fully Convolutional Networks for Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 640-651. https://doi.org/10.1109/TPAMI.2016.2572683
- Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9351, Issue Cvd, pp. 234-241). https://doi.org/10.1007/978-3-319-24574-4_28
- 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
- Sovetkin, E., Achterberg, E. J., Weber, T., & Pieters, B. E. (2021). Encoder-Decoder Semantic Segmentation Models for Electroluminescence Images of Thin-Film Photovoltaic Modules. IEEE Journal of Photovoltaics, 11(2), 444-452. https://doi.org/10.1109/JPHOTOV.2020.3041240
- Estrada, S., Conjeti, S., Ahmad, M., Navab, N., & Reuter, M. (2018). Competition vs. Concatenation in Skip Connections of Fully Convolutional Networks (pp. 214-222). https://doi.org/10.1007/978-3-030-00919-9_25
- Howard, A., Sandler, M., Chen, B., Wang, W., Chen, L.-C., Tan, M., Chu, G., Vasudevan, V., Zhu, Y., Pang, R., Adam, H., & Le, Q. (2019). Searching for MobileNetV3. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 1314-1324. https://doi.org/10.1109/ICCV.2019.00140
- He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016-Decem, 770-778. https://doi.org/10.1109/CVPR.2016.90
- Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016). The Cityscapes Dataset for Semantic Urban Scene Understanding. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016-Decem, 3213-3223. https://doi.org/10.1109/CVPR.2016.350
- Paszke, A., Chaurasia, A., Kim, S., & Culurciello, E. (2016). ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation. 1-10. http://arxiv.org/abs/1606.02147
- Treml, M., Arjona-medina, J., Unterthiner, T., Durgesh, R., Friedmann, F., Schuberth, P., Mayr, A., Heusel, M., Hofmarcher, M., Widrich, M., Nessler, B., & Hochreiter, S. (2016). Speeding up Semantic Segmentation for Autonomous Driving. NIPS 2016 Workshop MLITS, Nips, 1-7. https://openreview.net/pdf?id=S1uHiFyyg%0Ahttps://openreview.net/forum?id=S1uHiFyyg
- Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., & Torr, P. H. S. (2015). Conditional Random Fields as Recurrent Neural Networks. 2015 IEEE International Conference on Computer Vision (ICCV), 2015 Inter, 1529-1537. https://doi.org/10.1109/ICCV.2015.179
- Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018). DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834-848. https://doi.org/10.1109/TPAMI.2017.2699184
- Liu, Z., Li, X., Luo, P., Loy, C.-C., & Tang, X. (2015). Semantic Image Segmentation via Deep Parsing Network. 2015 IEEE International Conference on Computer Vision (ICCV), 2015 Inter, 1377-1385. https://doi.org/10.1109/ICCV.2015.162