基于改进DeepLabV3+和迁移学习的鱼体表型信息提取方法OA

Semantic segmentation of fish body images is a key technology for detecting fish body surface characteristic parameters and group behavior.In order to improve segmentation accuracy,an improved DeepLabV3+fish body image segmentation model was proposed.The model integrated the lightweight module(MobileNetV3),introduced the attention module(ECA)and improved the the atrous spatial pyramid pooling(ASPP)structure and adopted transfer learning training methods.The training results of the classical U-Net,SegNet,PSPNet and the improved DeepLabV3+semantic segmentation network in the fish body image data set were compared.Experimental results showed that the improved DeepLabV3+network model had higher segmentation accuracy,feature extraction and inference efficiency in the integrated fish body image data set,with the mean intersection over union(MIoU)reaching 94.61%and the accuracy reaching 93.78%.A fish body model was used to detect the parameters of total length,body height,perimeter and area,and the error range between the average value and the true value was less than 3.74%.The improved DeepLabV3+network model elevated the accuracy of semantic segmentation of fish body image,and could provide a reference for the automatic measurement of fish body phenotype characteristic parameters.