As one of the world’s few large-scale aquaculture countries, the mechanization, output and efficiency are low in Chinese aquaculture industry.with the development of industrial automation technology and computer image processing technology, aquaculture will be automated, scientific management transformation. As a specific application of computer recognition technology, underwater image recognition technology is important to promote aquaculture industry automation and intelligence. In this paper, first of all introduces the existing research of underwater target image recognition, mainly presents the underwater image recognition technology based on deep learning. And then the current problems of underwater image recognition is summarized. Besides, the future development of underwater image recognition technology and its application in wisdom fishery is also discussed, which provides other scholar with some theoretical help and a comprehensive reference.
Recently there has been growing interest in learning probabilistic models that admit poly-time inference called tractable probabilistic models from data. Although they generalize poorly as compared to intractable models, they often yield more accurate estimates at prediction time. In this paper, we seek to further explore this trade-off between generalization performance and inference accuracy by proposing a novel, partially tractable representation called cutset Bayesian networks (CBNs). The main idea in CBNs is to partition the variables into two subsets X and Y, learn a (intractable) Bayesian network that represents P(X) and a tractable conditional model that represents P(Y|X). The hope is that the intractable model will help improve generalization while the tractable model, by leveraging Rao-Blackwellised sampling which combines exact inference and sampling, will help improve the prediction accuracy. To compactly model P(Y|X), we introduce a novel tractable representation called conditional cutset networks (CCNs) in which all conditional probability distributions are represented using calibrated classifiers—classifiers which typically yield higher quality probability estimates than conventional classifiers. We show via a rigorous experimental evaluation that CBNs and CCNs yield more accurate posterior estimates than their tractable as well as intractable counterparts.
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