Research On The Method Of Fishing Nets Detection Based On Deep Learning

With the development of society,the value of the ocean has been paid more and more attention by human beings.People begin to develop and utilize the ocean from different angles.However,in the process of marine fishing,fishing nets are often damaged and scattered in the sea,which leads to more and more incidents of fishing nets winding propellers and have caused great direct or indirect losses to the shipping industry and environments.At present,there is still no effective detection technology and reports for fishing net detection.Aiming at the current situation of fishing net detection in abysmal sea,the detection has short distance,low accuracy,poor real-time performance and lack of spatial information,so this paper proposes a fishing net target detection method based on deep learning,the specifically work of this paper is as follows:1)Based on the analysis of the current situation of underwater target detection and deep learning at home and abroad,this paper combined with the characteristics of fishing nets,proposes an underwater laser scanning system to collect underwater fishing nets data with high accuracy and long distance,and then obtain underwater optical data with clear fishing nets.2)Paper briefly introduces the structure unit of traditional convolution neural network.Aiming at the problem of single form and small amount of data collected by experiment,an improved optimization algorithm and interpolation of image generation antagonism network generated by interpolation image are proposed to generate pseudo-fishing net image with evaluation index,which can expand the original data and lay a big data foundation for the follow-up deep learning target detection.3)The advantage and disadvantage of classical deep learning feature network structure and mainstream methods of deep learning target detection are analyzed.The paper proposes an improved feature extract net,multi-scale feature fusion with enlarged receptive field and combined the method with regions suggestion.On the basis of guaranteeing the real-time performance of target detection,the accuracy of detection is improved,and the two-dimensional detection of fishing net is realized.4)The principle of pixel space imaging and binocular location is briefly introduced.This paper analyses the problems of low matching efficiency and long matching time of binocular feature points,and then proposes the extraction of corner feature point and matching algorithm.According to the results of two-dimensional detection of fishing net target,the number of binocular feature points extracted is increased,and the accuracy of location detection is improved.5)Comparing this research algorithm with the corresponding algorithm at present,this paper makes experiments to compare the indicator and effects,and then transplants it into embedded system to carry it on underwater robot for visual navigation.The experimental results show that the deep learning fishing net detection method proposed in this paper can accurately detect fishing nets autonomous in real-time and three-dimensional with high accuracy.In addition,it has great practical and application value for underwater target detection.