Semantic road segmentation using encoder-decoder architectures

被引：2

作者：

Latsaheb B. ^{[1
]}

Sharma S. ^{[1
]}

Hasija S. ^{[1
]}

机构：

[1] Indian Institute of Information Technology, Pune

来源：

Multimedia Tools and Applications | 2025年 / 84卷 / 9期

关键词：

Deep learning; DenseNet; Encoder-decoder architecture; ResNet; Road segmentation;

D O I：

10.1007/s11042-024-19175-y

中图分类号：

学科分类号：

摘要：

Road detection is a fundamental task in autonomous driving, making accurate and efficient road area segmentation essential for the safe and precise navigation of autonomous vehicles. This paper proposes various models for road segmentation, employing an encoder-decoder architecture for fully automatic segmentation of road areas. As part of the encoder, this work explores different models, such as ResNet50V2, DenseNet121, DenseNet169, and DenseNet201, and utilizes them in one of the few dedicated methods for road area segmentation. Here, the dataset, derived from the Mapillary Vistas Dataset, has been meticulously pre-processed to convert it into a binary segmentation problem for road detection, comprising 8041 training images and 919 validation images with their respective masks. The models were trained on our dataset, achieving the highest Dice coefficient value of 99.61% on the training dataset and 93.85% on the validation dataset using the DenseNet169 encoder model. This research contributes to advancing the state-of-the-art in road segmentation for autonomous driving applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

引用

页码：5961 / 5983

页数：22

共 45 条

[11]

Cordts M., Omran M., Ramos S., Scharwachter T., Enzweiler M., Benenson R., Franke U., Roth S., Schiele B., The cityscapes dataset. In: CVPR Workshop on the future of datasets in vision, 2, (2015)

[12]

Doshi K., Yilmaz Y., Road damage detection using deep ensemble learning, In: 2020 IEEE International Conference on Big Data (Big Data), IEEE, pp. 5540-5544, (2020)

[13]

Gagliardi V., Giammorcaro B., Bella F., Sansonetti G., Deep neural networks for asphalt pavement distress detection and condition assessment, Earth Resources and environmental remote sensing/GIS Applications XIV, SPIE, pp. 251-262, (2023)

[14]

Geiger A., Lenz P., Stiller C., Urtasun R., Vision meets robotics: The kitti dataset, Int J Robotics Res, 32, 11, pp. 1231-1237, (2013)

[15]

Ghandorh H., Boulila W., Masood S., Koubaa A., Ahmed F., Ahmad J., Semantic segmentation and edge detection—approach to road detection in very high resolution satellite images, Remote Sens, 14, 3, (2022)

[16]

Han C., Zhao Q., Zhang S., Chen Y., Zhang Z., Yuan J., Yolopv2: Better, Faster, Stronger for Panoptic Driving Perception, (2022)

[17]

He K., Zhang X., Ren S., Sun J., Deep Residual Learning for Image Recognition, (2015)

[18]

Huang G., Liu Z., van Der Maaten L., Weinberger K.Q., Densely connected convolutional networks, In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700-4708, (2017)

[19]

Khairdoost N., Beauchemin S.S., Bauer M.A., Road lane detection and classification in urban and suburban areas based on cnns, pp. 450-457, (2021)

[20]

Kolle M., Walter V., Schmohl S., Soergel U., Learning on the edge: Benchmarking active learning for the semantic segmentation of als point clouds, ISPRS Ann Photogrammetry, Remote Sens Spatial Inf Sci, 10, pp. 945-952, (2023)

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