Abstract
As a contactless security technology, X-ray security inspection machine is widely used in the detection of dangerous object in all kinds of densely populated public places to ensure the safety. Unlike a natural image, various objects overlapping with each other can be observed in an X-ray image for its perspectivity. It brings us a challenge that the traditional NMS (Non-maximum suppression) algorithm will suppress the less significant objects. In this paper, we propose a Smoother Soft NMS based on the difference in aspect ratios and areas of different object bounding boxes to improve the accuracy of overlapping object detection. We also propose a special data augmentation method to simulate the generation of complex samples of overlapping objects. On our dataset, we boost the mean Average Precision of ResNet-101 FPN from 89.44% to 96.67% and Cascade R-CNN from 96.43% to 97.21%. Detector trained by Smoother Soft NMS has a significant improvement in overlapping cases.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: a literature survey. ACM Comput. Surv. (CSUR) 35(4), 399–458 (2003)
Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: DeepFace: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–1708 (2014)
Sivic, J., Zisserman, A.: Video google: a text retrieval approach to object matching in videos. In: Null, p. 1470. IEEE (2003)
Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8. IEEE (2007)
Bouwmans, T., Zahzah, E.H.: Robust PCA via principal component pursuit: a review for a comparative evaluation in video surveillance. Comput. Vis. Image Underst. 122, 22–34 (2014)
Ma, X., et al.: Vehicle traffic driven camera placement for better metropolis security surveillance. In: IEEE Intelligent Systems (2018)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. arXiv preprint arXiv:1512.00567 (2015)
Szegedy, C., Ioffe, S., Vanhoucke, V.: Inception-v4, inception-resnet and the impact of residual connections on learning. arXiv preprint arXiv:1602.07261 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385 (2015)
Girshick, R.B., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR, pp. 580–587 (2014)
Girshick, R.B.: Fast R-CNN. In: ICCV, pp. 1440–1448 (2015)
Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS, pp. 91–99 (2015)
Redmon, J., Divvala, S., Girshick, R., et al.: You Only Look Once: Unified, Real-Time Object Detection. ArXiv preprint arXiv:1506.02640
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
He, K., Gkioxari, G., Dollár, P., et al.: Mask R-CNN. In: IEEE International Conference on Computer Vision, pp. 2980–2988. IEEE Computer Society (2017)
Rosenfeld, A., Thurston, M.: Edge and curve detection for visual scene analysis. IEEE Trans. Comput. 5, 562–569 (1971)
Bodla, N., Singh, B., Chellappa, R., Davis, L.S.: Soft-NMS improving object detection with one line of code. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 5562–5570. IEEE (2017)
Lin, T.Y., Dollár, P., Girshick, R., et al.: Feature Pyramid Networks for Object Detection. ArXiv preprint arXiv:1612.03144
Shrivastava, A., Gupta, A., Girshick, R.: Training region-based object detectors with online hard example mining. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 761–769 (2016)
Wang, X., Shrivastava, A., Gupta, A.: A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection. ArXiv preprint arXiv:1704.03414
Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., et al.: Generative adversarial nets. In: International Conference on Neural Information Processing Systems, pp. 2672–2680. MIT Press (2014)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)
Cai, Z.: Nuno Vasconcelos. Cascade R-CNN: Delving into high quality object detection. ArXiv preprint arXiv:1712.00726
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Lin, C., Bao, X., Zhou, X. (2019). Smoother Soft-NMS for Overlapping Object Detection in X-Ray Images. In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (eds) Intelligence Science and Big Data Engineering. Visual Data Engineering. IScIDE 2019. Lecture Notes in Computer Science(), vol 11935. Springer, Cham. https://doi.org/10.1007/978-3-030-36189-1_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-36189-1_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36188-4
Online ISBN: 978-3-030-36189-1
eBook Packages: Computer ScienceComputer Science (R0)