获取渔业养殖鱼类生长态势的人工测量方法费时费力,且影响鱼的正常生长。为了实现水下鱼体信息动态感知和快速无损检测,该研究提出立体视觉下动态鱼体尺寸测量方法。通过双目立体视觉技术获取三维信息,再通过Mask-RCNN(Mask Region Conv...获取渔业养殖鱼类生长态势的人工测量方法费时费力,且影响鱼的正常生长。为了实现水下鱼体信息动态感知和快速无损检测,该研究提出立体视觉下动态鱼体尺寸测量方法。通过双目立体视觉技术获取三维信息,再通过Mask-RCNN(Mask Region Convolution Neural Network)网络进行鱼体检测与精细分割,最后生成鱼表面的三维点云数据,计算得到自由活动下多条鱼的外形尺寸。试验结果表明,长度和宽度的平均相对误差分别在4.7%和9.2%左右。该研究满足了水产养殖环境下进行可视化管理、无接触测量鱼体尺寸的需要,可以为养殖过程中分级饲养和合理投饵提供参考依据。展开更多
鱼群图像和视频的自动检测,在科学养殖与监管、海洋渔业监测等领域有广泛应用。为了有效提高鱼群检测的精确度,一些学者已经提出了基于深度学习的方法,但是实时高效的检测出鱼群的位置还未得到较好的解决。本文利用计算机视觉与深度学...鱼群图像和视频的自动检测,在科学养殖与监管、海洋渔业监测等领域有广泛应用。为了有效提高鱼群检测的精确度,一些学者已经提出了基于深度学习的方法,但是实时高效的检测出鱼群的位置还未得到较好的解决。本文利用计算机视觉与深度学习方法相结合,提出了一种基于YOLO算法的端到端鱼群检测方法,通过提取整张图像的特征,利用卷积运算与非极大值抑制处理后直接估计出该图像内各目标位置信息,处理速度大幅度提升。同时,针对光线较暗的水下场景,算法依然能够实现场景中鱼群的检测定位。在Labeled Fishes in the Wild图像数据集上验证了本算法,可以达到30帧/秒的处理速度,对实时视频中鱼群的检测精度能够达到90%以上。展开更多
针对光照不均、噪声大、拍摄质量不高的夜晚水下环境,为实现夜晚水下图像中鱼类目标的快速检测,利用计算机视觉技术,提出了一种基于改进Cascade R-CNN算法和具有色彩保护的MSRCP(Multi-scale Retinex with color restoration)图像增强...针对光照不均、噪声大、拍摄质量不高的夜晚水下环境,为实现夜晚水下图像中鱼类目标的快速检测,利用计算机视觉技术,提出了一种基于改进Cascade R-CNN算法和具有色彩保护的MSRCP(Multi-scale Retinex with color restoration)图像增强算法的夜晚水下鱼类目标检测方法。首先针对夜晚水下环境的视频数据,根据时间间隔,截取出相应的夜晚水下鱼类图像,对截取的原始图像进行MSRCP图像增强。然后采用DetNASNet主干网络进行网络训练和水下鱼类特征信息的提取,将提取出的特征信息输入到Cascade R-CNN模型中,并使用Soft-NMS候选框优化算法对其中的RPN网络进行优化,最后对夜晚水下鱼类目标进行检测。实验结果表明,该方法解决了夜晚水下环境中的图像降质、鱼类目标重叠检测问题,实现了对夜晚水下鱼类目标的快速检测,对夜晚水下鱼类图像目标检测的查准率达到95.81%,比Cascade R-CNN方法提高了11.57个百分点。展开更多
The estimation of fish mass is one of the most basic and important tasks in aquaculture.Acquiring the mass of fish at different growth stages is of great significance for feeding,monitoring the health status of fish,a...The estimation of fish mass is one of the most basic and important tasks in aquaculture.Acquiring the mass of fish at different growth stages is of great significance for feeding,monitoring the health status of fish,and making breeding plans to increase production.The existing estimation methods for fish mass often stay in the 2D plane,and it is difficult to obtain the 3D information on fish,which will lead to the error.To solve this problem,a multi-view method was proposed to obtain the 3D information of fish and predict the mass of fish through a two-stage neural network with an edge-sensitive module.In the first stage,the side-and downward-view images of the fish and some 3D information,such as side area,top area,length,deflection angle,and pitch angle,were captured to estimate the size of the fish through two vertically placed cameras.Then the area of the fish at different views was estimated accurately through the pre-trained image segmentation neural network with an edgesensitive module.In the second stage,a fully connected neural network was constructed to regress the fish mass based on the 3D information obtained in the previous stage.The experimental results indicate that the proposed method can accurately estimate the fish mass and outperform the existing estimation methods.展开更多
The morphological features of fish,such as the body length,the body width,the caudal peduncle length,the caudal peduncle width,the pupil diameter,and the eye diameter are very important indicators in smart mariculture...The morphological features of fish,such as the body length,the body width,the caudal peduncle length,the caudal peduncle width,the pupil diameter,and the eye diameter are very important indicators in smart mariculture.Therefore,the accurate measurement of the morphological features is of great significance.However,the existing measurement methods mainly rely on manual measurement,which is operationally complex,low efficiency,and high subjectivity.To address these issues,this paper proposes a scheme for segmenting fish image and measuring fish morphological features indicators based on Mask R-CNN.Firstly,the fish body images are acquired by a home-made image acquisition device.Then,the fish images are preprocessed and labeled,and fed into the Mask R-CNN for training.Finally,the trained model is used to segment fish image,thus the morphological features indicators of the fish can be obtained.The experimental results demonstrate that the proposed scheme can segment the fish body in pure and complex backgrounds with remarkable performance.In pure background,the average relative errors(AREs)of all indicators measured all are less than 2.8%,and the AREs of body length and body width are less than 0.8%.In complex background,the AREs of all indicators are less than 3%,and the AREs of body length and body width is less than 1.8%.2020 China Agricultural University.Production and hosting by Elsevier B.V.on behalf of KeAi.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).1.Introduction With the advancing of its scientific and technological capabilities,China has made great achievements in the mariculture.The production accounts for more than 70%of the world’s overall mariculture output[1].The measurement of body length,body width and other morphological features of fish have wide application prospects in smart mariculture.Due to the difference in the quality and feeding ability of the Juvenile fish,the growth of the fish in the same pond is significantly differe展开更多
文摘获取渔业养殖鱼类生长态势的人工测量方法费时费力,且影响鱼的正常生长。为了实现水下鱼体信息动态感知和快速无损检测,该研究提出立体视觉下动态鱼体尺寸测量方法。通过双目立体视觉技术获取三维信息,再通过Mask-RCNN(Mask Region Convolution Neural Network)网络进行鱼体检测与精细分割,最后生成鱼表面的三维点云数据,计算得到自由活动下多条鱼的外形尺寸。试验结果表明,长度和宽度的平均相对误差分别在4.7%和9.2%左右。该研究满足了水产养殖环境下进行可视化管理、无接触测量鱼体尺寸的需要,可以为养殖过程中分级饲养和合理投饵提供参考依据。
文摘鱼群图像和视频的自动检测,在科学养殖与监管、海洋渔业监测等领域有广泛应用。为了有效提高鱼群检测的精确度,一些学者已经提出了基于深度学习的方法,但是实时高效的检测出鱼群的位置还未得到较好的解决。本文利用计算机视觉与深度学习方法相结合,提出了一种基于YOLO算法的端到端鱼群检测方法,通过提取整张图像的特征,利用卷积运算与非极大值抑制处理后直接估计出该图像内各目标位置信息,处理速度大幅度提升。同时,针对光线较暗的水下场景,算法依然能够实现场景中鱼群的检测定位。在Labeled Fishes in the Wild图像数据集上验证了本算法,可以达到30帧/秒的处理速度,对实时视频中鱼群的检测精度能够达到90%以上。
文摘针对光照不均、噪声大、拍摄质量不高的夜晚水下环境,为实现夜晚水下图像中鱼类目标的快速检测,利用计算机视觉技术,提出了一种基于改进Cascade R-CNN算法和具有色彩保护的MSRCP(Multi-scale Retinex with color restoration)图像增强算法的夜晚水下鱼类目标检测方法。首先针对夜晚水下环境的视频数据,根据时间间隔,截取出相应的夜晚水下鱼类图像,对截取的原始图像进行MSRCP图像增强。然后采用DetNASNet主干网络进行网络训练和水下鱼类特征信息的提取,将提取出的特征信息输入到Cascade R-CNN模型中,并使用Soft-NMS候选框优化算法对其中的RPN网络进行优化,最后对夜晚水下鱼类目标进行检测。实验结果表明,该方法解决了夜晚水下环境中的图像降质、鱼类目标重叠检测问题,实现了对夜晚水下鱼类目标的快速检测,对夜晚水下鱼类图像目标检测的查准率达到95.81%,比Cascade R-CNN方法提高了11.57个百分点。
基金funded by Guangdong Provincial Natural Science Foundation General Project(Grant No.2023A1515011700)GuangDong Basic and Applied Basic Research Foundation(Grant No.2022A1515110007)+1 种基金the Guangdong Provincial Natural Science Foundation General Project(Grant No.2023A1515012869)GDAS'Project of Science and Technology Development(Grant No.2022GDASZH-2022010108).
文摘The estimation of fish mass is one of the most basic and important tasks in aquaculture.Acquiring the mass of fish at different growth stages is of great significance for feeding,monitoring the health status of fish,and making breeding plans to increase production.The existing estimation methods for fish mass often stay in the 2D plane,and it is difficult to obtain the 3D information on fish,which will lead to the error.To solve this problem,a multi-view method was proposed to obtain the 3D information of fish and predict the mass of fish through a two-stage neural network with an edge-sensitive module.In the first stage,the side-and downward-view images of the fish and some 3D information,such as side area,top area,length,deflection angle,and pitch angle,were captured to estimate the size of the fish through two vertically placed cameras.Then the area of the fish at different views was estimated accurately through the pre-trained image segmentation neural network with an edgesensitive module.In the second stage,a fully connected neural network was constructed to regress the fish mass based on the 3D information obtained in the previous stage.The experimental results indicate that the proposed method can accurately estimate the fish mass and outperform the existing estimation methods.
基金This research was supported by the National Natural Science Foundation of China(61963012,61961014)the Natural Science Foundation of Hainan Province,China(619QN195,618QN218)+1 种基金the Key R&D Project of Hainan Province,China(ZDYF2018015)Collaborative Innovation Fund Project of Tianjin University-Hainan University(HDTDU201907).
文摘The morphological features of fish,such as the body length,the body width,the caudal peduncle length,the caudal peduncle width,the pupil diameter,and the eye diameter are very important indicators in smart mariculture.Therefore,the accurate measurement of the morphological features is of great significance.However,the existing measurement methods mainly rely on manual measurement,which is operationally complex,low efficiency,and high subjectivity.To address these issues,this paper proposes a scheme for segmenting fish image and measuring fish morphological features indicators based on Mask R-CNN.Firstly,the fish body images are acquired by a home-made image acquisition device.Then,the fish images are preprocessed and labeled,and fed into the Mask R-CNN for training.Finally,the trained model is used to segment fish image,thus the morphological features indicators of the fish can be obtained.The experimental results demonstrate that the proposed scheme can segment the fish body in pure and complex backgrounds with remarkable performance.In pure background,the average relative errors(AREs)of all indicators measured all are less than 2.8%,and the AREs of body length and body width are less than 0.8%.In complex background,the AREs of all indicators are less than 3%,and the AREs of body length and body width is less than 1.8%.2020 China Agricultural University.Production and hosting by Elsevier B.V.on behalf of KeAi.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).1.Introduction With the advancing of its scientific and technological capabilities,China has made great achievements in the mariculture.The production accounts for more than 70%of the world’s overall mariculture output[1].The measurement of body length,body width and other morphological features of fish have wide application prospects in smart mariculture.Due to the difference in the quality and feeding ability of the Juvenile fish,the growth of the fish in the same pond is significantly differe
