Abstract
Decrease in visibility causes many difficulties in vision, tracking. Current classic object detection techniques do not give satisfying results in less visibility. It is essential to detect and recognize the objects under such conditions and devise a better object detection mechanism. The paper proposes a solution to this problem by using a multi step approach that uses Saliency techniques and modern object detection algorithms to obtain the desired results. The distorted image is enhanced via a deep neural network for visibility enhancement. The image frame of a better quality undergoes saliency techniques so that less visible objects are visible. Faster Region-based Convolutional Neural Network (R-CNN) then runs on the saliency output to yield bounding boxes for all the objects. The coordinates of the bounding boxes are then applied on the original image thus detecting all the objects in a distorted image with less visibility.
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References
Kopf, J., et al.: Deep photo: model-based photograph enhancement and viewing. In: ACM Transactions on Graphics, Proceedings of SIGGRAPH Asia, vol. 27, pp. 116:1–116:10 (2008)
Lu, W., Sun, X., Li, C.: A new method of object saliency detection in foggy images. In: Zhang, Y.-J. (ed.) ICIG 2015. LNCS, vol. 9217, pp. 206–217. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21978-3_19
Hussain, F., Jeong, J.: Visibility enhancement of scene images degraded by foggy weather conditions with deep neural networks. J. Sens. 2016, Article ID 3894832, 9 (2016)
Amirul Islam, M.D., Kalash, M., Bruce, N.D.B.: Revisiting salient object detection: simultaneous detection, ranking, and subitizing of multiple salient objects. In: Computer Vision and Pattern Recognition (2018). arXiv:1803.05082
Girshick, R.: Fast R-CNN. arXiv:1504.08083 (2015)
Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal net- works. arXiv preprint arXiv:1506.01497 (2015)
Guo, J., Ren, T., Bei, J., Zhu, Y.: Salient object detection in RGB-D image based on saliency fusion and propagation. In: Proceedings of the 7th International Conference on Internet Multi-media Computing and Service, p. 59. ACM (2015)
Song, H., Liu, Z., Du, H., Sun, G., Le Meur, O., Ren, T.: Depth-aware salient object detec-tion and segmentation via multiscale discriminative saliency fusion and bootstrap learning. IEEE Trans. Image Process. 26(9), 4204–4216 (2017)
Guo, F., Tang, J., Cai, Z.: Fusion strategy for single image dehazing. Int. J. Digital Content Technol. Appl. 7(1), 19 (2013)
Ansia, S., Aswathy, A.L.: Single image haze removal using white balancing and saliency map. Procedia Comput. Sci. 46, 12–19 (2015)
Erdem, E.: A region covariance-based visual attention model for RGB-D images. Int. J. Intell. Syst. Appl. Eng. 4(4), 128–134 (2016)
Qu, L., He, S., Zhang, J., Tian, J., Tang, Y., Yang, Q.: RGBD salient object detection via deep fusion. IEEE Trans. Image Process. 26(5), 2274–2285 (2017)
Du, J.: Understanding of object detection based on CNN family and YOLO. In: Journal of Physics: Conference Series, vol. 1004, p. 012029. IOP Publishing (2018)
Erdem, E., Erdem, A.: Visual saliency estimation by nonlinearly integrating features using region covariances. J. Vis. 13(4), 11–11 (2013)
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. TPAMI 40(4), 834–848 (2018)
Lakshmanaprabu, S.K., Mohanty, S.N., Shankar, K., Arunkumar, N.: Optimal deep learning model for classification of lung cancer on CT images. Future Gener. Comput. Syst. 19(1), 374–382 (2019)
Rajeshwari, P., Abhishek, P., Srikanth, P., Vinod, T.: Object detection: an overview. Int. J. Trend Sci. Res. Dev. (IJTSRD) 3(1), 1663–1665 (2019)
Tian, Y., Yang, G., Wang, Z., et al.: Apple detection during different growth stages in orchards using the improved YOLO-V3 model. Comput. Electron. Agric. 157, 417–426 (2019)
Wu, X., Sahoo, D., Hoi, S.C.H.: Recent advances in deep learning for object detection. arXiv:1908.03673v1 [cs.CV], August 2019
Zhao, Z.-Q., Zheng, P., Xu, S., Wu, X.: Object detection with deep learning. arXiv:1807.05511 [cs.CV], April 2019
Alexey, A.B.: Apple detection during different growth stages in orchards using the improved YOLO-V3 model (2018). https://github.com/AlexeyAB/Yolo
Kim, S., Ji, Y., Lee, K.: An effective sign language learning with object detection based ROI segmentation. In: Second IEEE International Conference on Robotic Computing (IRC), Laguna Hills, CA, pp. 330–333 (2018)
Nwankpa, C., Ijomah, W., Gachagan, A., Marshall, S.: Activation functions: comparison of trends in practice and research for deep learning. arXiv:1811.03378 [cs.LG], November 2018
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. arXiv:1703.06870 [cs.CV], January 2018
Munera, S., Amigo, J.M., Blasco, J., Cubero, S., Talens, P., Alexios, N.: Ripeness monitoring of two cultivars of nectarine using VIS-NIR hyperspectral reflectance imaging. J. Food Eng. 214(3), 29–39 (2017)
Bargoti, S., Underwood, J.: Deep fruit detection in orchards. In: IEEE International Conference on Robotics and Automation (ICRA), Singapore, pp. 3626–3633 (2017). https://doi.org/10.1109/ICRA.2017.7989417
Zhang, Y., Sohn, K., Villegas, R., Pan, G., Lee, H.: Improving object detection with deep convolutional networks via bayesian optimization and structured prediction. arXiv:1504.03293 [cs.CV], January 2016
Lu, Y., Javidi, T., Lazebnik, S.: Adaptive object detection using adjacency and zoom prediction. arXiv:1512.07711 [cs.CV], April 2016
Santagapita, P.R., Tylewicz, U., Panarese, V., Rocculi, P., Dalla Rosa, M.: Non-destructive assessment of kiwifruit physic-chemical parameters to optimize the osmotic dehydration process: a study on FT-NIR spectroscopy. J. Biosyst. Eng. 142(2), 101–129 (2016)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. arXiv:1506.01497v3 [cs.CV], January 2016
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. arXiv:1506.02640 [cs.CV], May 2016
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)
O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 [cs.NE], December (2015)
Girshick, R.: Fast R-CNN. arXiv:1504.08083 [cs.CV], September 2015
Erhan, D., Szegedy, C., Toshev, A., Anguelov, D.: Scalable object detection using deep neural networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2147–2154 (2014)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. arXiv:1311.2524 [cs.CV], October 2014
Jia, K., Wang, X., Tang, X.: Image transformation based on learning dictionaries across image spaces. IEEE Trans. Pattern Anal. Mach. Intell. 35(2), 367–380 (2013)
Common Objects in Context. http://cocodataset.org/
Open Images Dataset V5. https://storage.googleapis.com/openimages/web/index.html
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Nguyen, H.H.C., Nguyen, D.H., Nguyen, V.L., Nguyen, T.T. (2020). Smart Solution to Detect Images in Limited Visibility Conditions Based Convolutional Neural Networks. In: Hernes, M., Wojtkiewicz, K., Szczerbicki, E. (eds) Advances in Computational Collective Intelligence. ICCCI 2020. Communications in Computer and Information Science, vol 1287. Springer, Cham. https://doi.org/10.1007/978-3-030-63119-2_52
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