With the development of society, the number of old people is increasing. Slow response, osteoporosis and vision loss threaten the health of the elderly. The fall of this problem is an important ...factor that threatens the health of the elderly. In order to reduce the damage caused by falls, this paper based on the human skeleton map for fall prediction. First using OPENPOSE get the bone map and make it into a data set. Then using transfer learning to train the data set to get a new model Finally, the new model is used to predict the fall. The innovations in this paper are to take bone maps from 2D images and use bone maps to make fall predictions. The bone map is predicted using a convolutional neural network. The final experimental results show that the new model obtained through transfer learning has an accuracy rate of 91.7%. This result fully demonstrates the validity of the proposed model.
•The innovations in this paper are to obtain bone maps from 2D images.•This paper uses convolutional neural networks for fall prediction.•This paper uses OPENPOSE to get the bone map and make data sets.•This paper uses transfer learning to train the data set.
The development of computer technology and computer vision has had a significant positive impact on the daily lives of blind people, especially in efforts to improve their navigation abilities. This ...research has the main aim of introducing a superior object detection method, especially in supporting the sustainability and effectiveness of navigation for the blind. The main focus of the research is the use of YOLOv8, the latest version of YOLO, as an object detection method, and distance measurement technology from OpenCV. The main challenge to be addressed involves improving the accuracy and performance of object detection, which is an important key to ensuring safe and effective navigation for blind people. In this context, blind people often face obstacles in their mobility, especially when walking around environments that may be full of obstacles or obstacles. Therefore, better object detection methods become essential to ensure the identification of nearby objects, which may involve obstacles or potential threats, thereby preventing possible accidents or difficulties in daily commuting. Involving YOLOv8 as an object detection method provides the advantage of a high level of accuracy, although with a slight increase in detection duration and GPU power consumption compared to previous versions. The research results show that YOLOv8 provides a low error rate, with an average error percentage of 3.15%, indicating very optimal results. Using a combined performance evaluation approach of YOLOv8 and OpenCV distance measurement metrics, this research not only seeks to improve accuracy but also efficiency in detection time and power consumption. This research makes an important contribution in presenting technological solutions that can help improve mobility and safety for blind people, bringing a real positive impact in facilitating their daily lives.
Advancing in variable scopes in network technology, many new technologies were developed. Security issues were important, especially in the detection and recognition of people using variable methods ...like face details. Sensors have been used widely in recent days to support security systems. Sensors are devices used to convert any type of signals into electrical signals that are recorded to be processed later. These signals can be viewed by the user in several ways. Sensors increased in the development stage that can be integrated with operating systems, data storage systems, processing units, communication units, and any other function units. Detection and recognition systems were developed into a new level of technology. Some systems like figure print and palm lines face many problems because the possible change of the skin structure can be faced in time. So, these methods faced a certain problem and limitations that caused them to search for other methods more accurate. This search aims to create a new method for face detection and recognition depending on sensors. Most of the methods used for face recognition depend on OpenCV libraries that give good accuracy and time recovery availability. On the other hand, practical applications were developed to increase the accuracy of these systems like SeetaFace and YouTu methods. Three methods of detection were important to be detected too to increase the accuracy of the whole system which are the side face detection, the occlusion detection, and the face expressions. Then these data were compared to create the whole accuracy result of the system.
This paper suggests a novel method for the dynamic measurement and control of single-crystal tungsten micro-tip corrosion in order to address the problems of poor repeatability, low control ...precision, and the inability to set control tip diameters in the conventional single-crystal tungsten micro-tip electrochemical corrosion process. This technique begins by capturing experimental images using a video microscope, performing edge detection and contour searching to extract boundary information of the single-crystal tungsten micro-tip, and subsequently calculating and displaying the instantaneous tip diameter. Subsequently, a target diameter for the micro-tip is set, and when the detected value reaches this target, the software automatically cuts off the corrosion current. Experimental results demonstrate that this technique enables the production of high-quality single-crystal tungsten micro-tips with a diameter of approximately ⌀800 nm and exhibits excellent repeatability.
As the prominence of autonomous aerial vehicles continues to rise globally, the ability of surveillance carried out remotely is likely to become a key aspect. In large spaces, random security checks ...are performed by security guards and are quite inefficient since on-foot monitoring is extremely non-productive and slow. This paper proposes a remote surveillance solution integrated into an aerial vehicle that performs random inspections and streams the live video on a web dashboard which is a faster and more reliable solution. The paper presents how to design and implement an Unmanned Aerial Vehicle (UAV) with surveillance capabilities wherein the live stream will be remotely accessible on any device connected to the same network. The main purpose of this drone is to transmit live data/video to a web dashboard and hence run inferences to analyse the data. A custom-made drone is integrated with Raspberry Pi with a Universal Serial Bus (USB) camera. An attempt to stream drone video feeds on local devices has been made in this field. The proposed framework operates in the robot operating system (ROS) integrated with the gazebo simulator and is designed to enable the software to communicate with the hardware. Two primary methodologies are utilised; simulations and experimental. The expected outcome should satisfy the need to view the drone’s live video feed on a web dashboard for remote surveillance and computer vision application. At the resolution of 640 × 480, the latency in the live stream varied from 1 to 3 s with 10 frame rates per second. The analysis of the flight logs of the drone suggests that it meets the desired stability required for smooth video streaming. The total estimate calculated to implement this system is Rs. 70,000 including contingencies and spare parts required for experimentation. The proposed prototype appears to be a viable solution for security solutions required by multiple large space owners on the study's findings.
Abstract
This work proposes a process to detect the wear and tear of car tires. Tire is the only part of the road that does not interact with the road. The condition of the wheel should therefore be ...monitored in a timely manner for safe driving. Tired fatigue occurs due to limitations such as that the tread limit is less than 1.6 cm, the damage to the rubber, where there are pipes around 4 to 5, the affected tire. We look at some of the above limitations of tire wear testing using computer viewing techniques such as opencv and convolutional neural networks. Opencv and convolutional neural networks are widely used for object detection and image classification. We used these methods and obtained 90.90% accuracy, with which we can predict tire wear to avoid dangerous accidents..
Abstract
Convolutional neural network is an important neural network model in deep learning and a common algorithm in computer vision problems. From the perspective of practical application ...scenarios, this paper studies whether padding in convolutional neural network convolution layer weakens the image edge information. In order to eliminate the background factor, this paper select MNIST dataset as the research object, move the 0-9 digital image to the specified image edge by clearing the white area pixels in the specified direction, and use OpenCV to realize bilinear interpolation to scale the image to ensure that the image dimension is 28×28. The convolution neural network is built to train the original dataset and the processed dataset, and the accuracy rates are 0.9892 and 0.1082 respectively. In the comparative experiment, padding cannot solve the problem of weakening the image edge weight well. In the actual digital recognition scene, it is necessary to consider whether the core recognition area in the input image is at the edge of the image.
One of the fast-growing areas of deep learning using artificial intelligence is computer vision, becoming increasingly popular. It is a growing field of research that seeks to create techniques to ...help computers “see” and recognize the information of digital images for instance videos and photographs. Object detection is a computer vision method that enables us to recognize objects in an image or video and locate them. This article describes an efficient shape-based object identification method and its displacement in real-time using OpenCV library of programming roles mostly targeted at computer vision and Raspberry Pi with camera module.
Abstract
With the rapid growth of the number of motor vehicles in the city, traffic congestion is more serious every day, part of it is caused by the coding delay with the red light on, not real ...traffic jams, now we need a control system that can really change the traffic flow. In this paper, ITLCS (intelligent traffic signal control system) based on OpenCV image processing technology is proposed to adjust the timing of traffic signal according to road density, instead of setting a level that is balanced with other lanes, so that high-load traffic lanes can be used for a long time. The camera facing the roadway in the system takes pictures of the driving route, then takes pictures of the driving density of pedestrians and vehicles, and compares each image through processing technology, after the system is processed, the traffic light signal timing can be adjusted immediately, which greatly reduces the time spent on the inactive green light and can effectively deal with the traffic congestion problem.
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