Object extraction of yellow catfish based on RGHS image enhancement and improved YOLOv5 network
Aiming at the problems of poor underwater visibility,low accuracy and slow speed of object extraction,a yellow catfish object extraction model based on RGHS algorithm and improved YOLOv5 was proposed.Firstly,in order to solve the image quality problems caused by uneven illumination and high noise,RGHS algorithm was used to enhance the brightness of yellow catfish image.Then,C3ghost and CA attention mechanisms were introduced into the YOLOv5 backbone network,and gnConv was used to replace the common convolution in the neck part,so as to establish an improved YOLOv5 model and improve the target extraction accuracy of yellow catfish.The results show that compared with YOLOv5,the AP value,accuracy rate and recall rate of the improved model are increased by 2.76%,3.16%and 3.1%respectively,the F1 value is increased by 0.03,the memory occupied by the improved model is reduced by 2.3 MB,and the reasoning time of a single image is reduced by 0.001 s.Meanwhile,compared with the YOLOv4,SSD,Faster-RCNN and YOLOx models,the AP values of the improved models are increased by 3.27%,8.63%,2.48%and 2.52%respectively.The improved YOLOv5 model based on RGHS image enhancement can significantly improve the target extraction accuracy of yellow catfish while maintaining a fast speed,which can provide useful reference for the study of fish status monitoring methods.
万方数据
维普
