Recognition and counting algorithm of underwater sea cucumbers based on YOLOv4 network
In order to meet the requirements of automatic harvesting and high-efficiency measurement of sea cucumbers in intelligent aquaculture,a recognition and counting algorithm of underwater sea cucumbers based on YOLOv4 is proposed in this paper.The algorithm preprocesses the data set by using dark channel prior defogging algorithm to enhance the detectability of image data,YOLOv4 Network is trained with the transfer learning method,and Swish function is used to replace the activation function in the backbone network to improve the detection performance of the self-built data set,a method based on the target centroid positioning offsets of adjacent frames is proposed to optimize target counting result.The experimental results show that the mAP of sea cucumber targets recognized by the algorithm of this paper reaches 91.0%,which is 4.5%、6.9%、5.0%and 29.9%higher than that recognized by original YOLOv4,YOLOv3,Faster R-CNN,and SDD,respectively.The RMSE between the number of sea cucumbers obtained by reducing repeated counts and the manual counting result is 29.8.The average counting precision(ACP)is 95.8%and the coefficient of determination(R2)is 0.998.
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