[1] LIU B C, CAI H Y, YANG S Y et al. Lightweight semantic segmentation network for autonomous driving scenarios[J]. Journal of Xidian University, 50, 118-128(2023).

[2] LI Y C, ZHANG X J, ZHANG M L et al. Object detection in autonomous driving scene based on improved Efficientdet[J]. Computer Engineering and Applications, 58, 183-191(2022).

[3] LAFFERTY J D, MCCALLUM A, PEREIRA F C N. Conditional random fields: probabilistic models for segmenting and labeling sequence data[C](2001).

[4] HOLSCHNEIDER M, KRONLAND-MARTINET R, MORLET J et al. A real-time algorithm for signal analysis with the help of the wavelet transform[C](1990).

[5] CHEN L C, PAPANDREOU G, KOKKINOS I et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs[J/OL](2014).

[6] EIGEN D, FERGUS R. Predicting depth, Surface normals and semantic labels with a common multi-scale convolutional architecture[C], 2650-2658(2015).

[7] CHEN L C, PAPANDREOU G, SCHROFF F et al. Rethinking atrous convolution for semantic image segmentation[J/OL](2017).

[8] ZHAO H S, SHI J P, QI X J et al. Pyramid scene parsing network[C], 6230-6239(2017).

[9] VASWANI A, SHAZEER N, PARMAR N et al. Attention is all you need[C](2017).

[10] HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C], 7132-7141(2018).

[11] WOO S, PARK J, LEE J Y et al. CBAM: convolutional block attention module[C], 3-19(2018).

[12] ZHAO H S, ZHANG Y, LIU S et al. PSANet: point-wise spatial attention network for scene parsing[C], 267-283(2018).

[13] LI X, ZHONG Z S, WU J L et al. Expectation-maximization attention networks for semantic segmentation[C], 9167-9176(2019).

[14] TIAN X, WANG L, DING Q. Review of image semantic segmentation based on deep learning[J]. Journal of Software, 30, 440-468(2019).

[15] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[C](2015).

[16] LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C], 3431-3440(2015).

[17] 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).

[18] RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation[C], 234-241(2015).

[19] LI H C, XIONG P F, FAN H Q et al. DFANet: deep feature aggregation for real-time semantic segmentation[C], 9522-9531(2019).

[20] KIRILLOV A, WU Y X, HE K M et al. PointRend: image segmentation as rendering[C], 9799-9808(2020).

[21] XIE S N, GIRSHICK R, DOLLÁR P et al. Aggregated residual transformations for deep neural networks[C], 1492-1500(2017).

[22] CORDTS M, OMRAN M, RAMOS S et al. The cityscapes dataset for semantic urban scene understanding[C], 3213-3223(2016).

[23] 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).

[24] FAN M Y, LAI S Q, HUANG J S et al. Rethinking BiSeNet for real-time semantic segmentation[C], 9716-9725(2021).

[25] XIE E Z, WANG W H, YU Z D et al. SegFormer: simple and efficient design for semantic segmentation with transformers[C], 12077-12090(2021).