Abstract
Autonomous car has achieved unprecedented improvement in object detection because of the high performance of deep convolutional neural networks, and now researches are devoted to more complex traffic scene parsing. In this paper, we present a novel traffic scene parsing algorithm by learning a fully combined convolutional network (FCCN). Our network improves the upsampling layer of a fully convolutional network, we add five unpooling layers after the final convolution layer, and each unpooling layer is corresponded to a former pooling layer. We then combine each pair of pooling and unpooling layers, add convolution layers after the combined layer. Since we find it is still hard to learn fine details or edge features of target objects, we propose a soft cost function for further improvement. Our cost function adds soft weights on different target objects. The weight of background is set as constantly one, and the weights for target objects are calculated dynamically, which should be larger than two. We evaluate our work on CamVid datasets. The results show that our FCCN achieves a considerable improvement in segmentation performance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gao, J., Xie, Z., Zhang, J., Wu, K.W.: Image semantic analysis and understanding: a review. Pattern Recog. Artif. Intell. 23(2), 191–202 (2010)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017)
Brostow, G.J., Fauqueur, J., Cipolla, R.: Semantic object classes in video: a high-definition ground truth database. Pattern Recogn. Lett. 30(2), 88–97 (2009)
Badrinarayanan, V., Alex, K., Roberto, C.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. arXiv preprint arXiv:1511.00561v3 (2016)
Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1520–1528 (2015)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). doi:10.1007/978-3-319-24574-4_28
Lin, G., Milan, A., Shen, C., Reid, I.: RefineNet: Multi-Path Refinement Networks with Identity Mappings for High-Resolution Semantic Segmentation. arXiv preprint arXiv:1611.06612v3 (2016)
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122v3 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Paszke, A., Chaurasia, A., Kim, S., Culurciello, E.: ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation. arXiv preprint arXiv:1606.02147v1 (2016)
Chen, L.C., Yang, Y., Wang, J., Xu, W., Yuille, A.L.: Attention to scale: Scale-aware semantic image segmentation. arXiv preprint arXiv:1511.03339v2 (2016)
Mostajabi, M., Yadollahpour, P., Shakhnarovich, G.: Feedforward semantic segmentation with zoom-out features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3376–3385 (2015)
Vedaldi, A., Lenc, K.: Matconvnet: convolutional neural networks for matlab. In: Proceedings of the 23rd ACM International Conference on Multimedia, pp. 689–692. ACM (2016)
Tighe, J., Lazebnik, S.: SuperParsing: scalable nonparametric image parsing with superpixels. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6315, pp. 352–365. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15555-0_26
Liu, B., Xuming, H.: Multiclass semantic video segmentation with object-level active inference. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4286–4294 (2015)
Tripathi, S., Belongie, S., Hwang, Y., Nguyen, T.: Semantic video segmentation: exploring inference efficiency. In: SoC Design Conference (ISOCC), pp. 157–158. IEEE (2015)
Kundu, A., Vibhav V., Vladlen, K.: Feature space optimization for semantic video segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3168–3175 (2016)
Acknowledgements
This work was supported by the Fundamental Research Funds for the Central Universities (20143436).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Wu, Y., Yang, T., Zhao, J., Guan, L., Li, J. (2017). Fully Combined Convolutional Network with Soft Cost Function for Traffic Scene Parsing. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_64
Download citation
DOI: https://doi.org/10.1007/978-3-319-63309-1_64
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-63308-4
Online ISBN: 978-3-319-63309-1
eBook Packages: Computer ScienceComputer Science (R0)