Abstract
A target detection method based on fisher discriminative Mask-RCNN is proposed to improve the ability of the object detection and segmentation of multi-category armored targets. The fisher discriminative layer is added to the classification branch and trained by imposing the Fisher discrimination criterion on loss function. Target segmentation is achieved by adding a mask branch network to generate binary target masks. Experimental results indicate that the proposed method can increase the mean Average Precision (mAP) by 1.3% on our dataset of four similar armored targets and achieve good segmentation results, which can better meet the need of military reconnaissance and other tasks
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Lin, T.Y., Goyal, P., Girshick, R.: Focal loss for dense object detection. In: IEEE International Conference on Computer Vision, pp. 2999–3007. IEEE Computer Society (2017)
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Ren, S., He, K., Girshick, R.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1137–1149 (2015)
He, X.T., Peng, Y.X., Zhao, J.J.: Fine-grained discriminative localization via saliency-guided faster R-CNN. In: ACM MM (2017)
Li, Z., Peng, C., Yu, G.: Light-head R-CNN. In: Defense of Two-Stage Object Detector. https://arxiv.org/abs/1711.07264
He, K.M., Gkioxari, G., Dollár, P.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)
He, X.T., Peng, Y.X., Zhao, J.J.: Fast fine-grained image classification via weakly supervised discriminative localization. IEEE Trans. Circ. Syst. Video Technol. 29, 1394–1407 (2018)
Dai, J., Li, Y., He, K.: R-FCN: object detection via region-based fully convolutional networks. In: Advances in Neural Information Processing Systems, pp. 379–387 (2016)
Cheng, G., Zhou, P., Han, J.: RIFD-CNN: rotation-invariant and fisher discriminative convolutional neural networks for object detection. In: Computer Vision and Pattern Recognition, pp. 2884–2893. IEEE (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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chen, D., Yang, C., Liu, Z., Zhang, X., Shi, S. (2019). Object Detection and Segmentation Method for Multi-category Armored Targets Based on CNN. In: Wang, Y., Huang, Q., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2019. Communications in Computer and Information Science, vol 1043. Springer, Singapore. https://doi.org/10.1007/978-981-13-9917-6_7
Download citation
DOI: https://doi.org/10.1007/978-981-13-9917-6_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9916-9
Online ISBN: 978-981-13-9917-6
eBook Packages: Computer ScienceComputer Science (R0)