Brand, A., A. Manandhar. Semantic Segmentation of Burned Areas in Satellite Images Using a U-Net-Based Convolutional Neural Network. – Int. Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 43, 2021, No B3, pp. 47-53.
Chen, L.-C., G. Papandreou, I. Kokkinos, K. Murphy, A. L. Yuille. Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected Crfs. 2014, arXiv, 1412.7062.
Girshick, R., J. Donahue, T. Darrell, J. Malik. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. – In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 580-587.
Hafiz, A. M., G. M. Bhat. A Survey on Instance Segmentation: State of the Art. – International Journal of Multimedia Information Retrieval, Vol. 9, 2020, pp. 171-189.
He, K., X. Zhang, S. Ren, J. Sun. Deep Residual Learning for Image Recognition. – In: Proc. of 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770-778.
Hu, J., L. Shen, G. Sun. Squeeze-and-Excitation Networks. – In: Proc. of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 7132-7141.
Huang, G., Z. Liu, L. Van Der Maaten, K. Weinberger. Densely Connected Convolutional Networks. – In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 4700-4708.
Lin, T. Y., M. Maire,, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, C. L. Zitnick. Microsoft Coco: Common Objects in Context. – In: Lecture Notes in Computer Science. Vol. 8693. 2014, pp. 740-755.
Long, D. T. Efficient DenseNet Model with Fusion of Channel and Spatial Attention for Facial Expression Recognition. – Cybernetics and Information Technologies, Vol. 24, 2024, No 1, pp. 171-189.
Padilla, R., S. L. Netto, E. A. B. da Silva. A Survey on Performance Metrics for Object-Detection Algorithms. – In: Proc. of 2020 Int. Conference on Systems, Signals and Image Processing, 2020, pp. 237-242.
Pan, H., Y. Hong, W. Sun, Y. Jia. Deep Dual-Resolution Networks for Real-Time and Accurate Semantic Segmentation of Traffic Scenes. – IEEE Transactions on Intelligent Transportation Systems, Vol. 24, 2023, pp. 3448-3460.
Panchal, S., M. Kokare. Resmu-Net: Residual Multi-Kernel u-Net for Blood Vessel Segmentation in Retinal Fundus Images. – Biomedical Signal Processing and Control, 2024, 90:105859.
Redmon, J., S. Divvala, R. Girshick, A. Farhadi. You Only Look Once: Unified, Real-Time Object Detection. – In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 779-788.
Ronneberger, O., P. Fischer, T. Brox. U-Net: Convolutional Networks for Biomedical Image Segmentation. – In: Lecture Notes in Computer Sciences. Vol. 9351. 2015, pp. 234-241.
Xu, J., Z. Xiong, S. Bhattacharyya. Pidnet: A Real-Time Semantic Segmentation Network Inspired by Pid Controllers. – In: Proc. of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 19529-19539.
Yu, C., J. Wang, C. Peng, C. Gao, G. Yu, N. Sang. Learning a Discriminative Feature Network for Semantic Segmentation. – In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 1857-1866.
Zhang, W., S. Chen, Y. Ma, Y. Liu, X. Cao. Etunet: Exploring Efficient Transformer Enhanced UNet for 3d Brain Tumor Segmentation. – Computers in Biology and Medicine, Vol. 171, 2024, 108005.