摘要
去鳞是鱼类加工重要的环节之一,快速无损地计算去鳞率可实现对去鳞设备的评估或对其运行状态的实时监测。针对中国现阶段鱼类去鳞率主要靠人工计算的现象,提出一种基于实例分割的花白鲢去鳞率计算方法,将去鳞后的鱼体分为无鱼鳞区域和有鱼鳞区域,通过改进Mask R-CNN进行分割,计算去鳞率。将注意力机制模块SENet加入以ResNet50为Backbone的特征提取网络中,并通过减层处理,使网络更注意有价值特征的同时减少运算时间,并通过改进Loss函数迫使网络优化方向偏向于掩膜分割。结果显示:以1950张花白鲢图像作为数据集,对改进的Mask R-CNN模型进行训练,精准率达到90.6%,召回率达到91.9%;经试验验证,去鳞率计算平均误差为4.7%。研究表明,该方法具有良好的检测鱼体去鳞率能力以及实时计算能力,可用于评估去鳞设备,也可用于实时监测。
Descaling is an important part of freshwater fish processing.The detection of descaling rate is of great significance in evaluating the descaling equipment and monitoring the equipment in real time during operation.In view of the situation that the calculation of descaling rate in China is mainly artificial at present,which is time-consuming and laborious,this paper proposes a descaling calculation method of silver carp based on image segmentation,which divides the descaled fish body into two regions:the region with scale and the region without scale.The regions are distinguished by the improved Mask R-CNN.The Deep learning methods have good anti-interference ability and multi-dimensional feature extraction ability,can be applied to complex environment.In this paper,the SENet module is added into the feature extraction network with ResNet50 as Backbone,and the operation time is reduced while the network pays more attention to the valuable features.By improving the Loss function,the network optimization direction is forced to favor mask segmentation.Conclusion:The AP and Recall of the modified Mask R-CNN model reached 90.6%and 91.9%respectively in the data set of 1950 silver carp images.Compared with the original network,AP increased by 19.7%and Recall increased by 16.8%.The experimental results show that the average error is 4.7%.The fish scales with small areas are still unclear,and the mask boundary segmentation is not smooth.However,compared with the manual detection,this method realizes the calculation of descaling rate with convenience,speed,non-destructive,low cost and high accuracy.The method has good ability in distinguishment of different regions of fish body and real-time computing ability,and can be used for evaluating descaling equipment and real-time monitoring.
作者
肖哲非
沈建
郑晓伟
徐文其
XIAO Zhefei;SHEN Jian;ZHENG Xiaowei;XU Wenqi(Fishery Machinery and Instrument Research Institute,Chinese Academy of Fishery Sciences,Shanghai 200092,China;Key Laboratory of Ocean Fishing Vessel and Equipment,Ministry of Agriculture,Shanghai 200092,China;National R&D Branch Center for Aquatic Product Processing Equipment,Shanghai 200092,China;Dalian Polytechnic University,Collaborative Innovation Center of Seafood Deep Processing,Dalian 116034,Liaoning,China)
出处
《渔业现代化》
CSCD
2022年第2期85-93,共9页
Fishery Modernization
基金
“十三五”国家重点研发计划重点专项(2019YFD0901801)。
