Background Social distancing is an effective way to reduce the spread of the SARS-CoV-2 virus.Many students and researchers have already attempted to use computer vision technology to automatically detect human beings...Background Social distancing is an effective way to reduce the spread of the SARS-CoV-2 virus.Many students and researchers have already attempted to use computer vision technology to automatically detect human beings in the field of view of a camera and help enforce social distancing.However,because of the present lockdown measures in several countries,the validation of computer vision systems using large-scale datasets is a challenge.Methods In this paper,a new method is proposed for generating customized datasets and validating deep-learning-based computer vision models using virtual reality(VR)technology.Using VR,we modeled a digital twin(DT)of an existing office space and used it to create a dataset of individuals in different postures,dresses,and locations.To test the proposed solution,we implemented a convolutional neural network(CNN)model for detecting people in a limited-sized dataset of real humans and a simulated dataset of humanoid figures.Results We detected the number of persons in both the real and synthetic datasets with more than 90%accuracy,and the actual and measured distances were significantly correlated(r=0.99).Finally,we used intermittent-layer-and heatmap-based data visualization techniques to explain the failure modes of a CNN.Conclusions A new application of DTs is proposed to enhance workplace safety by measuring the social distance between individuals.The use of our proposed pipeline along with a DT of the shared space for visualizing both environmental and human behavior aspects preserves the privacy of individuals and improves the latency of such monitoring systems because only the extracted information is streamed.展开更多
The retinal structure and visual acuity in Japanese flounder Paralichthys olivaceus at different stages of development were examined by light microscopy. The resolving power of the retina, the visual axis and the best...The retinal structure and visual acuity in Japanese flounder Paralichthys olivaceus at different stages of development were examined by light microscopy. The resolving power of the retina, the visual axis and the best visual field were estimated based on the distribution of cone cells in the retina. The visual system of the larvae appears poorly developed at hatching. The larvae with total length (TL) of less than 10 mm, have single cones only and the eyes were well pigmented. At 10-11 mm TL, most single cones fused to form double cones, with the single and double cones forming a mosaic pattern. From larvae to early juvenile the retina stretches, the cones increase in diameter and rods increase in number. Based on the highest density of the cones in the ventro temporal region, the visual axis was orientated up forward. The resolving power of the retina in 40-530 mm TL Japanese flounder was found to range from 25.1 to 11.5 min. The results indicated continual improvements in the visual system of the growing fish towards higher resolving power, visual acuity and sensitivity.展开更多
A localization method based on distance function of projected features is presented to solve the accuracy reduction or failure problem due to occlusion and blurring caused by smog, when dealing with vision based local...A localization method based on distance function of projected features is presented to solve the accuracy reduction or failure problem due to occlusion and blurring caused by smog, when dealing with vision based localization for target oil and gas wellhead （OGWH）. Firstly, the target OGWH is modeled as a cylinder with marker, and a vector with redundant parameter is used to describe its pose. Secondly, the explicit mapping relationship between the pose vector with redundant parameter and projected features is derived. Then, a 2D-point-to-feature distance function is proposed, as well as its derivative. Finally, based on this distance function and its derivative, an algorithm is proposed to estimate the pose of target OGWH directly according to the 2D image information, and the validity of the method is verified by both synthetic data and real image experiments. The results show that this method is able to accomplish the localization in the case of occlusion and blurring, and its anti-noise ability is good especially with noise ratio of less than 70%.展开更多
文摘Background Social distancing is an effective way to reduce the spread of the SARS-CoV-2 virus.Many students and researchers have already attempted to use computer vision technology to automatically detect human beings in the field of view of a camera and help enforce social distancing.However,because of the present lockdown measures in several countries,the validation of computer vision systems using large-scale datasets is a challenge.Methods In this paper,a new method is proposed for generating customized datasets and validating deep-learning-based computer vision models using virtual reality(VR)technology.Using VR,we modeled a digital twin(DT)of an existing office space and used it to create a dataset of individuals in different postures,dresses,and locations.To test the proposed solution,we implemented a convolutional neural network(CNN)model for detecting people in a limited-sized dataset of real humans and a simulated dataset of humanoid figures.Results We detected the number of persons in both the real and synthetic datasets with more than 90%accuracy,and the actual and measured distances were significantly correlated(r=0.99).Finally,we used intermittent-layer-and heatmap-based data visualization techniques to explain the failure modes of a CNN.Conclusions A new application of DTs is proposed to enhance workplace safety by measuring the social distance between individuals.The use of our proposed pipeline along with a DT of the shared space for visualizing both environmental and human behavior aspects preserves the privacy of individuals and improves the latency of such monitoring systems because only the extracted information is streamed.
基金Project 39970578 supported by the NSFCsupported by the Ministry of Education Foundation for University Key Teachers.
文摘The retinal structure and visual acuity in Japanese flounder Paralichthys olivaceus at different stages of development were examined by light microscopy. The resolving power of the retina, the visual axis and the best visual field were estimated based on the distribution of cone cells in the retina. The visual system of the larvae appears poorly developed at hatching. The larvae with total length (TL) of less than 10 mm, have single cones only and the eyes were well pigmented. At 10-11 mm TL, most single cones fused to form double cones, with the single and double cones forming a mosaic pattern. From larvae to early juvenile the retina stretches, the cones increase in diameter and rods increase in number. Based on the highest density of the cones in the ventro temporal region, the visual axis was orientated up forward. The resolving power of the retina in 40-530 mm TL Japanese flounder was found to range from 25.1 to 11.5 min. The results indicated continual improvements in the visual system of the growing fish towards higher resolving power, visual acuity and sensitivity.
基金supported by National Natural Science Foundation of China(No.61403226)the State Key Laboratory of Tribology of China(No.SKLT09A03)
文摘A localization method based on distance function of projected features is presented to solve the accuracy reduction or failure problem due to occlusion and blurring caused by smog, when dealing with vision based localization for target oil and gas wellhead （OGWH）. Firstly, the target OGWH is modeled as a cylinder with marker, and a vector with redundant parameter is used to describe its pose. Secondly, the explicit mapping relationship between the pose vector with redundant parameter and projected features is derived. Then, a 2D-point-to-feature distance function is proposed, as well as its derivative. Finally, based on this distance function and its derivative, an algorithm is proposed to estimate the pose of target OGWH directly according to the 2D image information, and the validity of the method is verified by both synthetic data and real image experiments. The results show that this method is able to accomplish the localization in the case of occlusion and blurring, and its anti-noise ability is good especially with noise ratio of less than 70%.
