[1] Xu Z Z, Peng L, Dai F F. Semantic segmentation method based on multiscale feature alignment and aggregation[J]. Laser & Optoelectronics Progress, 60, 0215004(2023).

[2] Tang L, Wan L, Wang T T et al. DECANet: image semantic segmentation method based on improved DeepLabV3+[J]. Laser & Optoelectronics Progress, 60, 0410002(2023).

[3] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C], 3431-344(2015).

[4] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[M]. Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science, 9351, 234-241(2015).

[5] Badrinarayanan V, Kendall A, Cipolla R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2481-2495(2017).

[6] Qiu Y F, Wen J Y. Image semantic segmentation based on combination of DeepLabV3+ and attention mechanism[J]. Laser & Optoelectronics Progress, 59, 0410008(2022).

[7] Zhao H S, Shi J P, Qi X J et al. Pyramid scene parsing network[C], 6230-6239(2017).

[8] Lin G S, Milan A, Shen C H et al. RefineNet: multi-path refinement networks for high-resolution semantic segmentation[C], 5168-5177(2017).

[9] Kirillov A, Girshick R, He K M et al. Panoptic feature pyramid networks[C], 6392-6401(2020).

[10] Chen L C, Zhu Y K, Papandreou G et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 833-851(2018).

[11] Cheng B W, Collins M D, Zhu Y K et al. Panoptic-DeepLab: a simple, strong, and fast baseline for bottom-up panoptic segmentation[C], 12472-12482(2020).

[12] Yang M K, Yu K, Zhang C et al. DenseASPP for semantic segmentation in street scenes[C], 3684-3692(2018).

[13] Yang K L, Zhang J M, Reiß S et al. Capturing omni-range context for omnidirectional segmentation[C], 1376-1386(2021).

[14] Huynh C, Tran A T, Luu K et al. Progressive semantic segmentation[C], 16750-16759(2021).

[15] Zhang Z, Zhang X, Peng C et al. ExFuse: enhancing feature fusion for semantic segmentation[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11214, 273-288(2018).

[16] Liu Z, Mao H Z, Wu C Y et al. A ConvNet for the 2020s[C], 11966-11976(2022).

[17] Xu C, Wang L. Image semantic segmentation method based on improved DeepLabv3+ network[J]. Laser & Optoelectronics Progress, 58, 1610008(2021).

[18] Hou Q B, Zhou D Q, Feng J S. Coordinate attention for efficient mobile network design[C], 13708-13717(2021).

[19] Yu C Q, Gao C X, Wang J B et al. BiSeNet V2: bilateral network with guided aggregation for real-time semantic segmentation[J]. International Journal of Computer Vision, 129, 3051-3068(2021).

[20] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[21] Sandler M, Howard A, Zhu M L et al. MobileNetV2: inverted residuals and linear bottlenecks[C], 4510-4520(2018).

[22] Chen L C, Papandreou G, Schroff F et al. Rethinking atrous convolution for semantic image segmentation[EB/OL]. https://arxiv.org/abs/1706.05587

[23] Kervadec H, Bouchtiba J, Desrosiers C et al. Boundary loss for highly unbalanced segmentation[EB/OL]. https://arxiv.org/abs/1812.07032

[24] Milletari F, Navab N, Ahmadi S A. V-net: fully convolutional neural networks for volumetric medical image segmentation[C], 565-571(2016).

[25] Nigam I, Huang C, Ramanan D. Ensemble knowledge transfer for semantic segmentation[C], 1499-1508(2018).

[26] Mostegel C, Maurer M, Heran N et al. Semantic drone dataset[EB/OL]. https://www.tugraz.at/index.php?id=22387

[27] Vu T H, Jain H, Bucher M et al. DADA: depth-aware domain adaptation in semantic segmentation[C], 7363-7372(2020).

[28] Wang L, Li D, Zhu Y S et al. Dual super-resolution learning for semantic segmentation[C], 3773-3782(2020).

[29] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[C](2015).

[30] Howard A, Sandler M, Chen B et al. Searching for MobileNetV3[C], 1314-1324(2020).

[31] Zhang H, Hui J X. A new semantic segmentation network with FPFN and dense ASPP[C], 799-804(2021).

[32] Chollet F. Xception: deep learning with depthwise separable convolutions[C], 1800-1807(2017).

[33] Chen L C, Papandreou G, Kokkinos I et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848(2018).