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- W2895979188 abstract "The problem that the underwater robot is winded by fishing nets is the most concerned. At present, there are still no effective methods which can detect fishing nets in real time to avoid obstacles. As fishing net images taken with traditional detection technique are blurry and have low definition, it is hard to identify fishing nets. In view of this phenomenon, we proposed a new underwater detection method in this paper and used underwater laser scanning system to collect clear fishing net images. Due to the high cost of data acquisition and the single amount of experiment data, we took advantage of a deep generative network to amplify original data. In fishing nets detection network, we used a residual network as new base net to deepen the network layers, then combined the method of regression with some region suggestion to detect fishing nets, which can improve the accuracy of fishing nets detection without affecting the real-time performance. Finally, the contrast experiment of laser fishing net images is carried out to verify the effectiveness of the proposed method." @default.
- W2895979188 created "2018-10-26" @default.
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- W2895979188 date "2018-07-01" @default.
- W2895979188 modified "2023-09-26" @default.
- W2895979188 title "Deep Generative Network and Regression Network for Fishing Nets Detection in Real-time" @default.
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- W2895979188 doi "https://doi.org/10.23919/chicc.2018.8483142" @default.
- W2895979188 hasPublicationYear "2018" @default.
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