Abstract
Traditional underwater image processing methods have low accuracy and slow recognition speed. Underwater multi-target recognition is an important part of multi-target tracking. To solve this problem, an underwater multi-target detection method based on multi-scale convolution neural network (MSC-CNN) is proposed. Firstly, according to the problem of low contrast caused by the absorption and attenuation of underwater light, using the attenuation theory to equalize the dark channel of image to reduce the interference noise of background light in order to enhance the feature signal of object. Secondly, using MSC-CNN to fuse the feature maps generated by different convolution layers in the form of down-sampling and 1*1 convolution to extract the feature. Using the idea of Faster R-CNN for reference, the region-of-interest network is used to select regions of interest and share convolution layers. Finally, Softmax Loss is used to calculate classification probability and non-maximum suppression to correct the border position. The accuracy and test time are taken as evaluation metrics, and the recognition effect of different methods for underwater image is analyzed by comparing with the existing top algorithms. The experimental results of underwater images show that this method has advantages under various evaluation metrics.
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Acknowledgment
This research received financial support from the Key R&D Project of Hainan province (Grant No: ZDYD2019020), the National Key R&D Program of China (Grant No: 2018YFB1404401 and 2018YFB1404403), the National Natural Science Foundation of China (Grant No: 61662019 and 61862020), the Education Department of Hainan Province (Grant No: Hnky2019-22), the Higher Education Reform Key Project of Hainan province (Hnjg2017ZD-1) and Academician Workstation in “Hainan” Intelligent Healthcare Technologies.
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Huang, S., Huang, M., Zhang, Y., Li, M. (2019). Under Water Object Detection Based on Convolution Neural Network. In: Ni, W., Wang, X., Song, W., Li, Y. (eds) Web Information Systems and Applications. WISA 2019. Lecture Notes in Computer Science(), vol 11817. Springer, Cham. https://doi.org/10.1007/978-3-030-30952-7_6
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