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Neural Processing Letters
Erschienen in: Neural Processing Letters 8/2023

19.07.2023

ContourNet: Research on Contour Based Nighttime Semantic Segmentation

verfasst von: Yang Yang, Changjiang Liu, Hao Li

Erschienen in: Neural Processing Letters | Ausgabe 8/2023

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Abstract

Due to the scarcity of nighttime semantic segmentation datasets and the high demand for network models, the development of semantic segmentation of nighttime scenes is still very slow. This paper proposes a new network model, ContourNet, which can model multi-level features. In addition, a separate contour network module is designed to accurately predict object contours, improving performance for objects far away, small, or with high contour continuity. A large number of experiments demonstrate that the ContourNet proposed in this paper can significantly improve the semantic segmentation ability of existing models for nighttime images, and can also improve the semantic segmentation accuracy of daytime images to a certain extent, with good generalization abilities. Specifically, after adding the contour module in this article, MIoU has increased by 5.1% on the night dataset Rebecca; MIoU has increased by 2.5% on the daytime dataset CamVid.
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Literatur
1.
Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):640–651
2.
Huang K, Shi B, Li X, Li X, Huang S, Li Y (2022) Multi-modal sensor fusion for auto driving perception: a survey. arXiv e-prints
3.
Luo S, Dai H, Shao L, Ding Y (2021) M3dssd: monocular 3d single stage object detector. In: 2021 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp. 6141–6150
4.
Yang Z, Xinjun H, Yudong Y (2021) Design and research of industrial robot control system based on machine vision. In: 2021 5th international conference on electronics, communication and aerospace technology (ICECA), pp. 209–212
5.
Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 1–1
6.
Peng D, Lei Y, Hayat M, Guo Y, Li W (2022) Semantic-aware domain generalized segmentation. In: 2022 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp. 2584–2595
7.
Zhang J, Cao Y, Wu Q (2021) Vector of locally and adaptively aggregated descriptors for image feature representation. Pattern Recogn 116(4):107952CrossRef
8.
9.
Kittler J (1983) On the accuracy of the sobel edge detector. Image Vis Comput 1(1):37–42CrossRef
10.
Arbelaez P, Maire M, Fowlkes C, Malik J (2011) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 33(5):898–916CrossRef
11.
Zhang J, Fan J, Yang J, Yu J (2022) Semisupervised image classification by mutual learning of multiple self-supervised models. Int J Intell Syst 37(5):3117–3141CrossRef
12.
Wei S, Wang X, Yan W, Xiang B, Zhang Z (2015) Deepcontour: a deep convolutional feature learned by positive-sharing loss for contour detection. Comput Vis Pattern Recognit
13.
Xie S, Tu Z (2015) Holistically-nested edge detection. Int J Comput Vision 125(1–3):3–18MathSciNet
14.
Soria X, Riba E, Sappa A (2020) Dense extreme inception network: towards a robust cnn model for edge detection. In: Workshop on applications of computer vision
15.
Teichmann M, Weber M, Zöllner JM, Cipolla R, Urtasun R (2016) Multinet: real-time joint semantic reasoning for autonomous driving. In: 2018 IEEE intelligent vehicles symposium (IV) pp. 1013–1020
16.
Cheng D, Meng G, Xiang S, Pan C (2017) Fusionnet: edge aware deep convolutional networks for semantic segmentation of remote sensing harbor images. IEEE J Sel Top Appl Earth Observ Remote Sensing 99:1–15
17.
Guo X, Yu L, Ling H (2016) Lime: low-light image enhancement via illumination map estimation. IEEE Trans Image Process 99:1–1MATH
18.
Wu W, Weng J, Zhang P, Wang X, Yang W, Jiang J (2022) Uretinex-net: retinex-based deep unfolding network for low-light image enhancement. In: 2022 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp. 5891–5900
19.
Ma L, Ma T, Liu R, Fan X, Luo Z (2022) Toward fast, flexible, and robust low-light image enhancement. 2022 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp. 5627–5636
20.
He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp. 770–778
21.
Tan X, Xu K, Cao Y, Zhang Y, Ma L, Lau R (2021) Night-time scene parsing with a large real dataset. IEEE Trans Image Process
22.
Brostow GJ, Fauqueur J, Cipolla R (2009) Semantic object classes in video: a high-definition ground truth database. Pattern Recogn Lett 30(2):88–97CrossRef
23.
Yu C, Wang J, Peng C, Gao C, Yu G, Sang N (2018) Bisenet: bilateral segmentation network for real-time semantic segmentation. In: European conference on computer vision
24.
Zhao J, Liu J, Fan DP, Cao Y, Yang J, Cheng MM (2019) Egnet: edge guidance network for salient object detection. 2019 IEEE/CVF international conference on computer vision (ICCV), pp. 8778–8787
25.
Nirkin Y, Wolf L, Hassner T (2021) Hyperseg: patch-wise hypernetwork for real-time semantic segmentation. Comput Vis Pattern Recognit
26.
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. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR) pp. 3213–3223
27.
Zhao H, Qi X, Shen X, Shi J, Jia J (2017) ICNet for real-time semantic segmentation on high-resolution images
28.
Li H, Xiong P, Fan H, Sun J (2020) Dfanet: deep feature aggregation for real-time semantic segmentation. In: 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR)
29.
Hu P, Heilbron FC, Wang O, Lin ZL, Sclaroff S, Perazzi F (2020) Temporally distributed networks for fast video semantic segmentation. In: 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp. 8815–8824
30.
Li H, Liu C, Yang Y (2023) Layernet: a one-step layered network for semantic segmentation at night. IEEE Comput Graph Appl
31.
Fu J, Liu J, Tian H, Fang Z, Lu H (2018) Dual attention network for scene segmentation. In: 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp. 3141–3149
Metadaten
Titel
ContourNet: Research on Contour Based Nighttime Semantic Segmentation
verfasst von
Yang Yang
Changjiang Liu
Hao Li
Publikationsdatum
19.07.2023
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 8/2023
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-023-11366-2

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