Research On Underwater Seafood Recognition Method Based On Image Enhancement And Deep Learning

With the quality of life improving,the demand for all kinds of seafood has increased.At present,trawling and artificial fishing are the main fishing methods for seafood,but these two fishing methods have obvious defects.Underwater robots can adapt to the environment more flexible,higher efficient and safely,using underwater robots to achieve intelligent fishing has become a development trend.It is necessary to realize the accurate identification of underwater seafood in order to carry out accurate fishing for the reason that the underwater environment is complex and changeable.Therefore,according to the characteristics of underwater image imaging,this paper proposes an underwater seafood recognition method based on the combination of image enhancement and deep learning.Due to the light been absorbted and scattered in the water,the degradation problems of color deviation,fogging of details and low contrast are common in underwater images,which will interfere with the feature learning and detection and recognition of seafood.Therefore,an image fusion based on dark channel prior improvement enhancement algorithm is proposed,which sharpens image edge features and enhances dark details through homologous filtering in CIELab color space.In RGB color space,MSRCR algorithm is used to improve color deviation and enhance color saturation.Finally,according to the dark channel prior images of the three processing results,multi-image and multi-channel weighted fusion is carried out to obtain the final enhanced image.At the same time,the processed image is compared with other algorithms,and the enhanced effect of the algorithm is verified by quality evaluation indexes such as point sharpness,Brenner gradient,Tenengrad gradient,energy gradient,UIQM,information entropy,MSE,PSNR,SSIM and SIFT feature detection,which prove that our algorithm can provides a better environment for the underwater target feature learning and recognition.In the detection and recognition of underwater seafood,YOLOv3 algorithm with high realtime and accuracy is used to train the recognition model.Aiming at the traditional YOLOv3 algorithm could not determine the initial center distance by generating the prior box by k-means clustering problem,the K-means++ clustering algorithm was improved.Aiming at the problem that static adjustment of learning rate of recognition model training can easily lead to model divergence,the cosine annealing attenuation method with Warmup is used to achieve the learning rate dynamic adjustment.On this basis,aiming at the degraded water image is not conducive to target feature extraction and recognition and detection problem,an underwater seafood recognition method based on image enhancement and improved YOLOv3 is proposed.Finally,the improved algorithm and the traditional YOLOv3 algorithm are trained on the open data set and the detection and recognition accuracy is analyzed.As the results show that,in the improved YOLOv3 algorithm,our algorithm can enhance the average accuracy(AP),and the average accuracy(m AP)is increased by about 3.3 percentage points.It is verified that our algorithm can improve the identification accuracy of underwater seafood effectively and has higher application value.Finally,for the verifying the effectiveness of the proposed algorithm,an underwater seafood recognition experiment is carried out.The experiment mainly includes image acquisition,enhancement processing,image annotation and detection and recognition analysis.Experimental data were obtained from island snorkeling and underwater scene simulation.The underwater camera is used for video collection and frame processing,and the algorithm is used for image enhancement processing,and the target detection and recognition of echinus and holothurian are carried out in this paper.For purpose of reflecting the more intuitively detection effect of the recognition model,Label Img software is used to mark the target object in the image.The marking information includes the target category and boundary box position information,which will be compared and analyzed with the result of the final detection and recognition.And the results show that the echinus target and holothurian target based on the proposed algorithm recognition accuracy can reach 90.78% and 74.68%,which verify the recognition method valid and practicably,and can meet the needs of underwater seafood identification task.