Algorithm Research And Application Of Bighead Carp Recognition Based On Deep Learning

Bighead carp,a typical plankton-eating fish,are distributed in most waters of China.Account for a large proportion of freshwater aquaculture in China,bighead carp are the main products of pond culture and reservoir fishery and play a decisive role in ensuring the supply of high-quality aquatic products and promoting the income of fishermen.Bighead carp not only have high nutritional value,but also play a positive role in the improvement of water quality as they usually feed on zooplankton and phytoplankton in the water,which is an indispensable part of maintaining the ecological balance.With the rapid development of national economy in recent years,"eating safely and healthily" rises public concern.More and more consumers have put forward higher demands on the quality of bighead carp.On the one hand,consumers pursuit a kind of bighead carp with no pollution,good water quality and no feed,but they are unable to identify the such quality of fish,which make them buying with suspicious.On the other hand,it is necessary for enterprises to consider how to eliminate the doubts consumers when they are making greater efforts to develop bighead carp farming.Therefore,it is particularly urgent and practical significance to carry out the research on differential identification of the ecological bighead carp in reservoir culture and the refined bighead carp in pond culture.For the fish quality recognition,the early research mainly combined with relative fish feature such as the fish shape,texture features,and implement computer vision technology and traditional machine learning algorithm.In recent years,more and more scholars have combined deep learning,transfer learning and other techniques to study fish identification,and achieved good classification results.However,there is still little research on the source tracing of the same species of fish.In this paper,the process of recognition of bighead carp were studied on the basis of the current situation of fish identification technology at home and abroad.Based on the above,a recognition method of bighead carp was proposed,and a recognition system of bighead carp was designed based on this method.The specific work is as follows:Firstly,the data collection system of bighead carp was constructed.On the basis of analyzing the index data of bighead carp,a more perfect data collection method is put forward.The data collection of reservoir ecological bighead carp and pond intensive bighead carp were completed and the data collection system of bighead carp was successfully constructed.Secondly,the depth feature extraction method of bighead carp image was studied.On the basis of analyzing the algorithm of bighead carp recognition,a method of depth feature extraction on bighead carp image is proposed.This method integrates the attention mechanism into the convolutional neural network,and learns the importance of bighead carp image characteristic channels,and applies the weight vector learned to the convolutional neural network,which improves the performance of the model as well as the interpretability.Thirdly,a recognition method of bighead carp based on multi-mode fusion was proposed.Based on the image data of bighead carp and the index data,a multi-mode fusion recognition model of bighead carp is constructed.And the validity of this model is proved by corresponding experiments.which is made up of the bighead carp image depth feature extraction model and the depth neural network model.The weighted outputs of the two models constitute the final classification result.Finally,the identification prototype system of bighead carp was developed.On the basis of the feasibility of the bighead carp recognition method,design the bighead carp prototype recognition system based on deep learning.Separate the whole system into bighead fish real-time identification system and bighead fish data acquisition system.also,the certain elaboration is carried on both overall frame and the system development work separately,which shows that the model constructed in this paper has certain practicability.