Detecting and tracking multiple targets simultaneously for space-based surveillance requires multiple cameras,which leads to a large system volume and weight. To address this problem, we propose a wide-field detection...Detecting and tracking multiple targets simultaneously for space-based surveillance requires multiple cameras,which leads to a large system volume and weight. To address this problem, we propose a wide-field detection and tracking system using the segmented planar imaging detector for electro-optical reconnaissance. This study realizes two operating modes by changing the working paired lenslets and corresponding waveguide arrays： a detection mode and a tracking mode. A model system was simulated and evaluated using the peak signal-to-noise ratio method. The simulation results indicate that the detection and tracking system can realize wide-field detection and narrow-field, multi-target, high-resolution tracking without moving parts.展开更多
For the rapid and accurate identification of cow reproduction and healthy behavior from mass surveillance video,in this study,400 head of young cows and lactating cows were taken as the research object and analyzed co...For the rapid and accurate identification of cow reproduction and healthy behavior from mass surveillance video,in this study,400 head of young cows and lactating cows were taken as the research object and analyzed cow behavior from the dairy activity area and milk hall ramp.The method of object recognition based on image entropy was proposed,aiming at the identification of motional cow object behavior against a complex background.Calculating a minimum bounding box and contour mapping were used for the real-time capture of rutting span behavior and hoof or back characteristics.Then,by combining the continuous image characteristics and movement of cows for 7 d,the method could quickly distinguish abnormal behavior of dairy cows from healthy reproduction,improving the accuracy of the identification of characteristics of dairy cows.Cow behavior recognition based on image analysis and activities was proposed to capture abnormal behavior that has harmful effects on healthy reproduction and to improve the accuracy of cow behavior identification.The experimental results showed that,through target detection,classification and recognition,the recognition rates of hoof disease and heat in the reproduction and health of dairy cows were greater than 80%,and the false negative rates of oestrus and hoof disease were 3.28%and 5.32%,respectively.This method can enhance the real-time monitoring of cows,save time and improve the management efficiency of large-scale farming.展开更多
Oyster is the largest cultured shellfish in the world,and it has high economic value.The plumpness of the Pacific oyster gonad has important implications for the quality and breeding of subsequent parents.At present,o...Oyster is the largest cultured shellfish in the world,and it has high economic value.The plumpness of the Pacific oyster gonad has important implications for the quality and breeding of subsequent parents.At present,only the conventional method of breaking their shells allows for the observation and study of the interior tissues of Pacific oysters.It is an important task to use computer technology for non-destructive sex detection of oysters and to select mature and full oysters for breeding.In this study,based on the multi-effect feature fusion network R-SINet algorithm,a CF-Net algorithm was designed through a boundary enhancement algorithm to detect inconspicuous objects that appear to be seamlessly embedded in the surrounding environment in nuclear magnetic resonance(NMR)images,effectively solving the problem of difficulty in distinguishing Pacific oyster gonads from background images.In addition,calculations were performed on the segmented gonadal regions to obtain a grayscale value difference map between male and female oysters.It was found that there were significant differences in grayscale values between females and males.This task allows for non-destructive detection of the gender of oysters.Firstly,a small animal magnetic resonance imaging(MRI)system was used to perform MRI on Pacific oysters,and a dataset of oyster gonads was established.Secondly,a gonadal segmentation model was created,and the Compact Pyramid Refinement Module and Switchable Excitation Model were applied to the R-SINet algorithm model to achieve multi-effect feature fusion.Then,the Convformer encoder,Token Reinforcement Module,and Adjacent Transfer Module were used together to form the CF-Net network algorithm,further improving the segmentation accuracy.The experimental results on the oyster gonad dataset have demonstrated the effectiveness of this method.Based on the segmentation results,it is possible to calculate the grayscale values of the gonadal region and obtain the distribution map of the grayscale value difference between male and female oysters.The results can provide a technical methodology for the non-destructive discrimination of oyster gender and later reproduction.展开更多
基金supported by the Foundation of Youth Innovation Promotion Association,Chinese Academy of Sciences(No.20150192)
文摘Detecting and tracking multiple targets simultaneously for space-based surveillance requires multiple cameras,which leads to a large system volume and weight. To address this problem, we propose a wide-field detection and tracking system using the segmented planar imaging detector for electro-optical reconnaissance. This study realizes two operating modes by changing the working paired lenslets and corresponding waveguide arrays： a detection mode and a tracking mode. A model system was simulated and evaluated using the peak signal-to-noise ratio method. The simulation results indicate that the detection and tracking system can realize wide-field detection and narrow-field, multi-target, high-resolution tracking without moving parts.
基金the Natural Science Foundation of Beijing(4172026)Capability Innovation Project of Beijing Academy of Agriculture and Forestry(KJCX20170706).
文摘For the rapid and accurate identification of cow reproduction and healthy behavior from mass surveillance video,in this study,400 head of young cows and lactating cows were taken as the research object and analyzed cow behavior from the dairy activity area and milk hall ramp.The method of object recognition based on image entropy was proposed,aiming at the identification of motional cow object behavior against a complex background.Calculating a minimum bounding box and contour mapping were used for the real-time capture of rutting span behavior and hoof or back characteristics.Then,by combining the continuous image characteristics and movement of cows for 7 d,the method could quickly distinguish abnormal behavior of dairy cows from healthy reproduction,improving the accuracy of the identification of characteristics of dairy cows.Cow behavior recognition based on image analysis and activities was proposed to capture abnormal behavior that has harmful effects on healthy reproduction and to improve the accuracy of cow behavior identification.The experimental results showed that,through target detection,classification and recognition,the recognition rates of hoof disease and heat in the reproduction and health of dairy cows were greater than 80%,and the false negative rates of oestrus and hoof disease were 3.28%and 5.32%,respectively.This method can enhance the real-time monitoring of cows,save time and improve the management efficiency of large-scale farming.
基金financially supported by the Shandong Province Key R&D Program Project(Grant 2022TZXD005)Shandong Province Science and Technology SMEs Innovation Capacity Enhancement Project(Grant 2021TSGC1003)Yantai City Key R&D Program Project(Grant 2022XCZX079).
文摘Oyster is the largest cultured shellfish in the world,and it has high economic value.The plumpness of the Pacific oyster gonad has important implications for the quality and breeding of subsequent parents.At present,only the conventional method of breaking their shells allows for the observation and study of the interior tissues of Pacific oysters.It is an important task to use computer technology for non-destructive sex detection of oysters and to select mature and full oysters for breeding.In this study,based on the multi-effect feature fusion network R-SINet algorithm,a CF-Net algorithm was designed through a boundary enhancement algorithm to detect inconspicuous objects that appear to be seamlessly embedded in the surrounding environment in nuclear magnetic resonance(NMR)images,effectively solving the problem of difficulty in distinguishing Pacific oyster gonads from background images.In addition,calculations were performed on the segmented gonadal regions to obtain a grayscale value difference map between male and female oysters.It was found that there were significant differences in grayscale values between females and males.This task allows for non-destructive detection of the gender of oysters.Firstly,a small animal magnetic resonance imaging(MRI)system was used to perform MRI on Pacific oysters,and a dataset of oyster gonads was established.Secondly,a gonadal segmentation model was created,and the Compact Pyramid Refinement Module and Switchable Excitation Model were applied to the R-SINet algorithm model to achieve multi-effect feature fusion.Then,the Convformer encoder,Token Reinforcement Module,and Adjacent Transfer Module were used together to form the CF-Net network algorithm,further improving the segmentation accuracy.The experimental results on the oyster gonad dataset have demonstrated the effectiveness of this method.Based on the segmentation results,it is possible to calculate the grayscale values of the gonadal region and obtain the distribution map of the grayscale value difference between male and female oysters.The results can provide a technical methodology for the non-destructive discrimination of oyster gender and later reproduction.
