In this paper, we propose an underwater object detection method, which uses improved spectral residual (SR) saliency detection and fuzzy segmentation. We adopt a two-phase mechanism, which divides visual object detection into detecting saliency map and image segmentation to obtain “proto object”. We compare the logarithmic spectrum differences between optical images in the atmosphere and in the water. Combining with the absorption characteristics of the propagation of light in water, we use the logarithmic spectrum of underwater images and logarithmic spectrums in R, G and B channels to generate new logarithmic spectrum, so as to highlight more object information and obtain better saliency map. Then, using Fuzzy c-Means (FCM) clustering method to segment saliency map, we gather better similar information of the object and highlight the entire body of the objects. We tested the effectiveness of our method in underwater object detection in different underwater optical environments. The results show that our method can eliminate most of the background noise and improve the accuracy of underwater visual object detection.
G.L.Foresti and S.Gentili, A vision based system for object detection in underwater images, Int J PatternRecognit Artif Intell14 (2000), 167–188.
2.
R.Schettini and S.Corchs, Underwater image processing: State of the art of restoration and image enhancement methods, Eurasip J Adv Signal Process2010 (2010), 746–752.
3.
W.Huibin, Z.Qian, W.Xin and C.Zhe, Object detection based on regional saliency and underwater optical prior knowledge, Chinese Journal of Scientific Instrument35(2) (2014), 387–397.
4.
M.M.Cheng, G.X.Zhang, N.J.Mitra, et al., Global contrast based salient region detection, Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, IEEE, 2011, pp. 409–416.
5.
L.Itti, C.Koch and E.Niebur, A model of saliency-based visual attention for rapid scene analysis, IEEE Trans on PAMI20(11) (1998), 1254–1259.
6.
X.Hou and L.Zhang, Saliency Detection: A Spectral Residual Approach, CVPR, 2007.
7.
B.Ali and I.Laurent, State-of-the-art in visual attention modeling, IEEE Trans on PAMI35(1) (2013), 185–206.
8.
C.Zhe, W.Xin, S.Zhen and W.Zhijian, Motion saliency detection using a temporal fourier transform, Optics & Laser Technology80 (2016), 1–15.
9.
S.Jie, F.Tanghuai, T.Min, Z.Qian, S.Zhen and H.Fengchen, A Biological Hierarchical Model Based Underwater Moving Object Detection, Computational and Mathematical Methods in Medicine, Hindawi Publishing Corporation, vol. 2014, Article ID 609801, 2014.
10.
W.Huibin, C.Yong and C.Zhe, Visual depth based feature calculation and underwater target tracking, Journal of Image and Graphics19(4) (2014), 534–540.
11.
S.C.Yu, T.Ura, T.Fujii and H.Kondo, Navigation of autonomous underwater vehicles based on artificial underwater landmarks, In Proceedings of the MTS/IEEE Oceans 2001, Honolulu, HI, USA, 2001.
12.
G.Dudek, M.Jenkin, C.Prahacs, A.Hogue, J.Sattar, P.Giguere and S.Simhon, A visually guided swimming robot, In Proceedings of the 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, AB, Canada, 2005.
13.
J.Sattar and G.Dudek, Robust servo-control for underwater robots using banks of visual filters, In Proceedings of the IEEE International Conference on Robotics and Automation, Kobe, 2009.
14.
C.Barat and M.J.Rendas, A robust visual attention system for detecting manufactured objects in underwater video, In In Proceedings of the MTS/IEEE Oceans, 18–22, Boston, Massachusetts, USA,2006.
15.
Z.Huang, G.Shan, J.Cheng and J.Sun, TRec: An efficient recommendation system for hunting passengers with deep neural networks, Neural Computing and Applications32 (2018), 1–14.
16.
D.Kim, D.Lee, H.Myung and H.T.Choi, Artificial landmark-based underwater localization for AUVs using weighted template matching, Intell Serv Robot7 (2014), 175–184.
D.R.Edgington, K.A.Salamy, M.Risi, R.E.Sherlock, D.Walther and C.Koch, Automated event detection in underwater video, In Proceedings of the OCEANS 2003, San Diego, CA, USA,2003.
19.
D.Walther, D.R.Edgington and C.Koch, Detection and tracking of objects in underwater video, In Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, USA, Volume 1, 2004, pp. 544–549.
20.
B.Christian and P.Ronald, A fully automated method to detect and segment a manufactured object in an underwater color image, EURASIP Journal on Advances in Signal Processing (2010), 1–10.
21.
J.Li, R.M.Eustice and M.Johnson-Roberson, High-level visual features for underwater place recognition, In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA,2015.
22.
D.L.Rizzini, F.Kallasi, F.Oleari and S.Caselli, Investigation of vision-based underwater object detection with multiple datasets, Int J Adv Robot Syst12 (2015), 77.
23.
M.C.Chuang, J.N.Hwang and K.Williams, A feature learning and object recognition framework for underwater fish images, IEEE Trans Image Process25 (2016), 1862–1872.
24.
C.Koch and T.Poggio, Predicting the visual world: Silence is golden, Nature Neuroscience22(1) (1999), 9–10.
25.
S.Q.Duntley, Light in the sea, J Optical Society of America53(2) (1963), 214–233.
26.
J.Y.Chiang and Y.C.Chen, Underwater image enhancement by wavelength compensation and dehazing, IEEE Trans Image Process21 (2012), 1756–1769.
27.
R.P.Nikhil and C.B.James, On cluster validity for the Fuzzy c-Means model, IEEE Trans on Fuzzy Systems3(3) (1995), 370–379.
28.
A.W.Smeulders, D.M.Chu, R.Cucchiara, S.Calderara, A.Dehghan and M.Shah, Visual tracking: An experimental survey, IEEE Trans Pattern Anal Mach Intell36 (2014), 1442–1468.
