Research On Underwater Small Target Image Recognition Technology For Aquaculture

With the continuous development of emerging technologies such as big data and artificial intelligence,underwater image recognition technology is more and more used in aquaculture.The underwater environment of aquaculture pond is turbid and complex.The images collected by underwater camera have serious color deviation,low brightness and clarity,which has a great impact on the recognition of underwater aquaculture.Small target breeding objects have small product and contain less feature information,which further increases the difficulty of recognition.Aiming at the problem of underwater small target recognition in aquaculture,this paper first improves the image quality and enhances the image features through image preprocessing,and then improves the yolov3 algorithm to be suitable for underwater small target recognition.The main research work of this paper is as follows:(1)aiming at the problems of high image blur,poor contrast,dark brightness and color distortion in underwater images,a two-stage scheme for underwater target recognition is proposed.The first stage is underwater image enhancement,and the second stage is underwater target recognition.(2)In the underwater image enhancement stage,in order to improve the image enhancement effect,it is divided into three steps.The first step is to white balance the image and stretch the pixels;The second step is gamma correction and brightness adjustment;The third step is to enhance the detail information of the identified target in the image by sharpening and dark channel a priori method.(3)In the target recognition stage,in order to improve the recognition accuracy and the recognition rate of small targets,it is improved in two parts: the residual network of yolov3 and the hierarchical connection of feature pyramid.On the residual network,in view of the good performance of mish activation function in yolov4,it is considered to be introduced into the improvement of residual network.The improved residual block is divided into two steps,and finally the two parts are stacked.In the part of convolution pyramid,because small targets are difficult to be recognized in the feature layer with large grid,this paper generates a feature layer with smaller grid through the upper convolution mechanism of convolution pyramid for small target recognition,and uses pruning technology to remove the feature layer with large grid.For the newly generated feature layer,you need to set anchors for it.In order to find the right anchors,this paper uses K-means + + clustering algorithm to set the anchors.Through the combination of deepening the network structure and fusing the shallower features,the accuracy of small target recognition in the target recognition stage is improved.In the image enhancement stage,the experiment is carried out according to the image enhancement method in this paper.The PSNR and uiqm evaluation indexes obtained through the experiment are the largest,the image distortion is the smallest,and the image quality such as color,contrast and clarity is the best.In the target recognition stage,the accuracy of the improved yolov3 model on the test set is95.83%,the recall rate is 91.16%,and the average accuracy is 87.36%.Compared with the improved algorithm,the recognition accuracy is improved by 2.3%,the average accuracy is improved by 2.6%,and the recall rate is basically unchanged.The experimental results show that the underwater small target image recognition method proposed in this study has good performance in aquaculture.