The near future has been envisioned as a collaboration of humans with mobile robots to help in the day-to-day tasks.In this paper,we present a viable approach for a real-time computer vision based object detection and...The near future has been envisioned as a collaboration of humans with mobile robots to help in the day-to-day tasks.In this paper,we present a viable approach for a real-time computer vision based object detection and recognition for efficient indoor navigation of a mobile robot.The mobile robotic systems are utilized mainly for home assistance,emergency services and surveillance,in which critical action needs to be taken within a fraction of second or real-time.The object detection and recognition is enhanced with utilization of the proposed algorithm based on the modification of You Look Only Once(YOLO)algorithm,with lesser computational requirements and relatively smaller weight size of the network structure.The proposed computer-vision based algorithm has been compared with the other conventional object detection/recognition algorithms,in terms of mean Average Precision(mAP)score,mean inference time,weight size and false positive percentage.The presented framework also makes use of the result of efficient object detection/recognition,to aid the mobile robot navigate in an indoor environment with the utilization of the results produced by the proposed algorithm.The presented framework can be further utilized for a wide variety of applications involving indoor navigation robots for different services.展开更多
The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety(OHS)is of prime importance.Like in other developing countries,this indust...The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety(OHS)is of prime importance.Like in other developing countries,this industry pays very little,rather negligible attention to OHS practices in Pakistan,resulting in the occurrence of a wide variety of accidents,mishaps,and near-misses every year.One of the major causes of such mishaps is the non-wearing of safety helmets(hard hats)at construction sites where falling objects from a height are unavoid-able.In most cases,this leads to serious brain injuries in people present at the site in general and the workers in particular.It is one of the leading causes of human fatalities at construction sites.In the United States,the Occupational Safety and Health Administration(OSHA)requires construction companies through safety laws to ensure the use of well-defined personal protective equipment(PPE).It has long been a problem to ensure the use of PPE because round-the-clock human monitoring is not possible.However,such monitoring through technological aids or automated tools is very much possible.The present study describes a systema-tic strategy based on deep learning(DL)models built on the You-Only-Look-Once(YOLOV5)architecture that could be used for monitoring workers’hard hats in real-time.It can indicate whether a worker is wearing a hat or not.The proposed system usesfive different models of the YOLOV5,namely YOLOV5n,YOLOv5s,YOLOv5 m,YOLOv5l,and YOLOv5x for object detection with the support of PyTorch,involving 7063 images.The results of the study show that among the DL models,the YOLOV5x has a high performance of 95.8%in terms of the mAP,while the YOLOV5n has the fastest detection speed of 70.4 frames per second(FPS).The proposed model can be successfully used in practice to recognize the hard hat worn by a worker.展开更多
Cerebral microbleeds are small chronic vascular diseases that occur because of irregularities in the cerebrum vessels.Individuals and elderly people with brain injury and dementia can have small microbleeds in their b...Cerebral microbleeds are small chronic vascular diseases that occur because of irregularities in the cerebrum vessels.Individuals and elderly people with brain injury and dementia can have small microbleeds in their brains.A recent study has shown that cerebral microbleeds could be remarkably risky in terms of life and can be riskier for patients with dementia.In this study,we proposed an efficient approach to automatically identify microbleeds by reducing the false positives in openly available susceptibility-weighted imaging(SWI)data samples.The proposed structure comprises two different pretrained convolutional models with four stages.These stages include(i)skull removal and augmentation,(ii)making clusters of data samples using the k-mean classifier,(iii)reduction of false positives for efficient performance,and(iv)transfer-learning classification.The proposed technique was assessed using the SWI dataset available for 20 subjects.For our findings,we attained an accuracy of 97.26%with a 1.8%false-positive rate using data augmentation on the AlexNet transfer learning model and a 1.1%false-positive rate with 97.89%accuracy for the ResNet 50 model with data augmentation approaches.The results show that our models outperformed the existing approach for the detection of microbleeds.展开更多
文摘The near future has been envisioned as a collaboration of humans with mobile robots to help in the day-to-day tasks.In this paper,we present a viable approach for a real-time computer vision based object detection and recognition for efficient indoor navigation of a mobile robot.The mobile robotic systems are utilized mainly for home assistance,emergency services and surveillance,in which critical action needs to be taken within a fraction of second or real-time.The object detection and recognition is enhanced with utilization of the proposed algorithm based on the modification of You Look Only Once(YOLO)algorithm,with lesser computational requirements and relatively smaller weight size of the network structure.The proposed computer-vision based algorithm has been compared with the other conventional object detection/recognition algorithms,in terms of mean Average Precision(mAP)score,mean inference time,weight size and false positive percentage.The presented framework also makes use of the result of efficient object detection/recognition,to aid the mobile robot navigate in an indoor environment with the utilization of the results produced by the proposed algorithm.The presented framework can be further utilized for a wide variety of applications involving indoor navigation robots for different services.
文摘The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety(OHS)is of prime importance.Like in other developing countries,this industry pays very little,rather negligible attention to OHS practices in Pakistan,resulting in the occurrence of a wide variety of accidents,mishaps,and near-misses every year.One of the major causes of such mishaps is the non-wearing of safety helmets(hard hats)at construction sites where falling objects from a height are unavoid-able.In most cases,this leads to serious brain injuries in people present at the site in general and the workers in particular.It is one of the leading causes of human fatalities at construction sites.In the United States,the Occupational Safety and Health Administration(OSHA)requires construction companies through safety laws to ensure the use of well-defined personal protective equipment(PPE).It has long been a problem to ensure the use of PPE because round-the-clock human monitoring is not possible.However,such monitoring through technological aids or automated tools is very much possible.The present study describes a systema-tic strategy based on deep learning(DL)models built on the You-Only-Look-Once(YOLOV5)architecture that could be used for monitoring workers’hard hats in real-time.It can indicate whether a worker is wearing a hat or not.The proposed system usesfive different models of the YOLOV5,namely YOLOV5n,YOLOv5s,YOLOv5 m,YOLOv5l,and YOLOv5x for object detection with the support of PyTorch,involving 7063 images.The results of the study show that among the DL models,the YOLOV5x has a high performance of 95.8%in terms of the mAP,while the YOLOV5n has the fastest detection speed of 70.4 frames per second(FPS).The proposed model can be successfully used in practice to recognize the hard hat worn by a worker.
基金This research was supported by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2021–2016–0–00312)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation).
文摘Cerebral microbleeds are small chronic vascular diseases that occur because of irregularities in the cerebrum vessels.Individuals and elderly people with brain injury and dementia can have small microbleeds in their brains.A recent study has shown that cerebral microbleeds could be remarkably risky in terms of life and can be riskier for patients with dementia.In this study,we proposed an efficient approach to automatically identify microbleeds by reducing the false positives in openly available susceptibility-weighted imaging(SWI)data samples.The proposed structure comprises two different pretrained convolutional models with four stages.These stages include(i)skull removal and augmentation,(ii)making clusters of data samples using the k-mean classifier,(iii)reduction of false positives for efficient performance,and(iv)transfer-learning classification.The proposed technique was assessed using the SWI dataset available for 20 subjects.For our findings,we attained an accuracy of 97.26%with a 1.8%false-positive rate using data augmentation on the AlexNet transfer learning model and a 1.1%false-positive rate with 97.89%accuracy for the ResNet 50 model with data augmentation approaches.The results show that our models outperformed the existing approach for the detection of microbleeds.
