Image Detection Model Research Articles

IoT Based on Potholes and Speed Breakers Detection and Alert System

In a world where 90 percent of people travel by road, the safety of the same becomes of utmost importance. And two of the most major issues a road traveler faces are Potholes and speed breakers. The total number of road accident deaths due to potholes in 2018, 2019, and 2020 stood at 2015, 2140 and 1471, respectively. Although speed breakers are intended to prevent traffic accidents, 49.6 per cent of crashes still happen in areas where there are speed breakers.
 This paper aims to present a method for locating, identifying, and documenting speed breakers and potholes. The suggested method will employ driving information collected by various sensors, such as vibrations, jerks, bumps, changes in distance from the base of the vehicle, etc., to detect and then store the location of the object in an online database. Additionally, a stream of images of the object spotted will be simultaneously taken and uploaded to a server, to analyze using an Image Detection model. This model will facilitate the live detection of potholes and speed breakers more accurately than previous solutions.
 Once this data is added to a database, it will be effectively used by government agencies to repair the roads as well as by daily commuters to avoid the route with more obstacles, when the database is supplied to mapping services like Google Maps.

Detection of COVID-19 Cases Based on Deep Learning with X-ray Images

Since the outbreak of COVID-19, the coronavirus has caused a massive threat to people’s lives. With the development of artificial intelligence technology, identifying key features in medical images through deep learning, infection cases can be screened quickly and accurately. This paper uses deep-learning-based approaches to classify COVID-19 and normal (healthy) chest X-ray images. To effectively extract medical X-ray image features and improve the detection accuracy of COVID-19 images, this paper extracts the texture features of X-ray images based on the gray level co-occurrence matrix and then realizes feature selection by principal components analysis (PCA) and t-distributed stochastic neighbor embedding (T-SNE) algorithms. To improve the accuracy of X-ray image detection, this paper designs a COVID-19 X-ray image detection model based on the multi-head self-attention mechanism and residual neural network. It applies the multi-head self-attention mechanism to the residual network bottleneck layer. The experimental results show that the multi-head self-attention residual network (MHSA-ResNet) detection model has an accuracy of 95.52% and a precision of 96.02%. It has a good detection effect and can realize the three classifications of COVID-19 pneumonia, common pneumonia, and normal lungs, proving the method’s effectiveness and practicability in this paper.

Application Research on Face Image Evaluation Algorithm of Deep Learning Mobile Terminal for Student Check-In Management.

With the increase in the number of data, the traditional shallow image features cannot meet the needs of image representation. As an important means of image research, deep learning network has been paid attention to. In the field of face image evaluation, deep learning algorithm has been introduced, and the recognition technology has gradually matured. Based on this, this paper studies the application of face image evaluation algorithm of deep learning mobile terminal for student check-in management. A face image detection model for student check-in management is constructed, and a deep learning network is used to realize face detection. A face detection algorithm based on candidate region joint deep learning network is designed, and a face key point detection method based on cascaded convolution network is proposed. Aiming at the low efficiency of face recognition and detection, the existing loss function is optimized, the extraction algorithm of face binary features is proposed, and experiments are designed to analyze the performance of the algorithm. The simulation results show that the face detection based on the improved deep learning network can shorten the retrieval time and improve the accuracy of face image classification.

Deep Neural Network-Based Sports Marketing Video Detection Research

With the rapid development of short video, the mode of sports marketing has diversified, and the difficulty of accurately detecting marketing videos has increased. Identifying certain key images in the video is the focus of detection, and then, analysis can effectively detect sports marketing videos. The research of video key image detection based on deep neural network is proposed to solve the problem of unclear and unrecognizable boundaries of key images for multiscene recognition. First, the key image detection model of the feedback network is proposed, and ablation experiments are conducted on a simple test set of DAVSOD. The experimental results show that the proposed model achieves better performance in both quantitative evaluation and visual effects and can accurately capture the overall shape of significant objects. The hybrid loss function is also introduced to identify the boundaries of key images, and the experimental results show that the proposed model outperforms or is comparable to the current state-of-the-art video significant object detection models in terms of quantitative evaluation and visual effects.

Multi-Fusion Approach for Wood Microscopic Images Identification Based on Deep Transfer Learning

With the wide increase in global forestry resources trade, the demand for wood is increasing day by day, especially rare wood. Finding a computer-based method that can identify wood species has strong practical value and very important significance for regulating the wood trade market and protecting the interests of all parties, which is one of the important problems to be solved by the wood industry. This article firstly studies the establishment of wood microscopic images dataset through a combination of traditional image amplification technology and Mix-up technology expansion strategy. Then with the traditional Faster Region-based Convolutional Neural Networks (Faster RCNN) model, the receptive field enhancement Spatial Pyramid Pooling (SPP) module and the multi-scale feature fusion of Feature Pyramid Networks (FPN) module are introduced to construct a microscopic image identification model based on the migration learning fusion model and analyzes the three factors (Mix-up, Enhanced SPP and FPN modules) affecting the wood microscopic image detection model. The experimental results show that the proposed approach can identify 10 kinds of wood microscopic images, and the accuracy rate has increased from 77.8% to 83.8%, which provides convenient conditions for further in-depth study of the microscopic characteristics of wood cells and is of great significance to the field of wood science.

Application of artificial intelligence based on deep learning in breast cancer screening and imaging diagnosis

In recent years, with the change of lifestyle in Europe and America, the incidence of breast cancer in Chinese women is increasing. In order to find the model of breast cancer image screening and diagnosis with higher accuracy and better classification performance, this paper mainly constructs the breast cancer CT image detection model and the breast cancer screening model based on the convolution and deconvolution neural network (CDNN) through the convolution neural network (CNN). In this paper, the fuzzy C-means clustering algorithm (FCM) is used to improve and optimize the image of breast cancer, and the experimental results are analyzed. The optimized kernel fuzzy C-means clustering algorithm was tested on a common dataset to segment the region of interest more accurately. Our experiments show that the new deep learning model of this paper improves the automatic classification performance of breast cancer. In this paper, the research results of deep learning are applied to the medical field, and a new method based on CNN model for breast cancer screening and diagnosis is proposed, which provides a new idea for improving the artificial intelligence assisted medical diagnosis method.

Infrared Image Detection of Substation Insulators Using an Improved Fusion Single Shot Multibox Detector

The thermographic diagnostic of substation insulators can facility to detect their insulation status in time. However, the detection effect of infrared insulator images is usually poor due to the complex background of substation scene images. In order to solve this problem and improve the automatic detection capability of substation equipment, an infrared insulator image detection model based on the improved feature fusion single shot multibox detector is proposed in this paper. The model combines multi-scale feature maps to generate a new feature pyramid, and designs a new feature enhancement module in the shallow network of the model to improve its ability to extract features of infrared images of substation insulators. Meanwhile, the clustering algorithm is used to calculate the target aspect ratio information of the dataset to realize the adaptive change of the aspect ratio of the default box. Furthermore, the transfer learning is employed to further improve the learning efficiency of the model. Before the formal experiment, the design scheme of feature enhancement module is confirmed by ablation studies. Experiments are performed on the collected infrared insulator images, instrument transformer images and Pascal VOC 2007 dataset. It is demonstrated experimentally that this model achieves excellent detection results.

Image Detection Model for Construction Worker Safety Conditions using Faster R-CNN

Many accidents occur on construction sites leading to injury and death. According to the Occupational Safety Health Administration (OSHA), falls, electrocutions, being struck-by-objects and being caught in or between an object were the four main causes of worker deaths on construction sites. Many factors contribute to the increase in accidents, and personal protective equipment (PPE) is one of the defense mechanisms used to mitigate them. Thus, this paper presents an image detection model about workers’ safety conditions based on PPE compliance by using the Faster Region-based Convolutional Neural Networks (R-CNN) algorithm. This experiment was conducted using Tensorflow involving 1,129 images from the MIT Places Database (from Scene Recognition) as a training dataset, and 333 anonymous dataset images from real construction sites for evaluation purposes. The experimental results showed 276 of the images being detected as safe, and an average accuracy rate of 70%. The strength of this paper is based on the image detection of the three PPE combinations, involving hardhats, vests and boots in the case of construction workers. In future, the threshold and image sharpness (low resolution) will be two main characteristics of further refinement in order to improve the accuracy rate.

Theory research of glare reduction based on the Fresnel principle

Glare reduction is the focus of much attention in the field of optical engineering owing to its vital role in high-quality image acquisition in photoelectric visual inspection technology. Based on Fresnel reflection theory, we have derived a polarization factor related to the glare intensity distribution and have obtained theoretical results showing that the factor plays a leading role in reducing glare. Based on the image detection model, we have derived a displacement factor which determines the position and the size of the glare area, and have obtained theoretical results showing that the factor plays a supporting role in reducing glare. The image gray-scale transformation curve is obtained by sampling image data under the different polarization and positions. And these image data are measured from certain object in the equivalent environment while the different conditions. Then the theories above have been justified and the whole theory of the polarization-based glare elimination method has been interpreted completely via actual experiments in glare elimination. As a result, reducing the intensity contrast between the glare area and its neighboring area is proved to be the essence of glare elimination. Furthermore, the glare can be nearly eliminated perfectly by adjusting the polarization factor and the displacement factor.

The Application of Computer Vision in Cultural Production Innovation

CCD technology is used in production process of cultural products to detect flatness, which reduces the error formation probability. This paper proposes an image detection model for cultural production flatness measurement based on computer vision feature point matching algorithm. The method uses the pixel frequency sorting method to divide pixel level of collected images, so as to obtain high precision pixel characteristics as the basis for measuring the flatness for cultural products' structure, and extracts image features of cultural products and carries out matching process to establish an image detection model for flatness measurement. The experimental results show that the proposed algorithm greatly improves the accuracy of flatness measurement for cultural product structure.