scholarly journals Single Shot Multibox Detector Automatic Polyp Detection Network Based on Gastrointestinal Endoscopic Images

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Xiaoling Chen ◽  
Kuiling Zhang ◽  
Shuying Lin ◽  
Kai Feng Dai ◽  
Yang Yun
Keyword(s):  
Deep Learning ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
Single Shot ◽  
Polyp Detection ◽  
Endoscopic Images ◽  
Efficient Detection ◽  
Diagnosis Rate ◽  
Traditional Pattern ◽  
Fully Connected

Purpose. In order to resolve the situation of high missed diagnosis rate and high misdiagnosis rate of the pathological analysis of the gastrointestinal endoscopic images by experts, we propose an automatic polyp detection algorithm based on Single Shot Multibox Detector (SSD). Method. In the paper, SSD is based on VGG-16, the fully connected layer is changed to a convolutional layer, and four convolutional layers with successively decreasing scales are added as a new network structure. In order to verify the practicability, it is not only compared with manual polyp detection but also with Mask R-CNN. Results. Multiple experimental results show that the mean Average Precision (mAP) of the SSD network is 95.74%, which is 12.4% higher than the manual detection and 5.7% higher than the Mask R-CNN. When detecting a single frame of image, the detection speed of SSD is 8.41 times that of manual detection. Conclusion. Based on the traditional pattern recognition algorithm and the target detection algorithm using deep learning, we select a variety of algorithms to identify and classify polyps to achieve efficient detection results. Our research demonstrates that deep learning has a lot of room for development in the field of gastrointestinal image recognition.

scholarly journals Intelligent Highway Traffic Detection Algorithm based on Deep Learning

2021 ◽  
Author(s):  
ming ji ◽  
Chuanxia Sun ◽  
Yinglei Hu
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Traffic Congestion ◽  
Road Network ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
Traffic Data ◽  
Vehicle Recognition ◽  
Environment Recognition

Abstract In order to solve the increasingly serious traffic congestion problem, an intelligent transportation system is widely used in dynamic traffic management, which effectively alleviates traffic congestion and improves road traffic efficiency. With the continuous development of traffic data acquisition technology, it is possible to obtain real-time traffic data in the road network in time. A large amount of traffic information provides a data guarantee for the analysis and prediction of road network traffic state. Based on the deep learning framework, this paper studies the vehicle recognition algorithm and road environment discrimination algorithm, which greatly improves the accuracy of highway vehicle recognition. Collect highway video surveillance images in different environments, establish a complete original database, build a deep learning model of environment discrimination, and train the classification model to realize real-time environment recognition of highway, as the basic condition of vehicle recognition and traffic event discrimination, and provide basic information for vehicle detection model selection. To improve the accuracy of road vehicle detection, the vehicle target labeling and sample preprocessing of different environment samples are carried out. On this basis, the vehicle recognition algorithm is studied, and the vehicle detection algorithm based on weather environment recognition and fast RCNN model is proposed. Then, the performance of the vehicle detection algorithm described in this paper is verified by comparing the detection accuracy differences between different environment dataset models and overall dataset models, different network structures and deep learning methods, and other methods.

scholarly journals Football Players’ Shooting Posture Norm Based on Deep Learning in Sports Event Video

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Guangliang Huang ◽  
Zhuangxu Lan ◽  
Guo Huang
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Motion Tracking ◽  
Research Direction ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
Target Motion ◽  
Football Players ◽  
Data Set ◽  
Offensive Tactics

Football is one of the favorite sports of people nowadays. Shooting is the ultimate goal of all offensive tactics in football matches. This is the most basic way to score a goal and the only way to score a goal. The choice and use of shooting technical indicators can have a great impact on the final result of the game. Therefore, how to improve the shooting technique of football players and how to adjust the shooting posture of football players are important issues faced by coaches and athletes. In recent years, deep learning has been widely used in various fields such as image classification and recognition and language processing. How to apply deep learning optimization to shooting gesture recognition is a very promising research direction. This article aims to study the football player’s shooting posture specification based on deep learning in sports event videos. Based on the analysis of target motion detection algorithm, target motion tracking algorithm, target motion recognition algorithm, and football shooting posture classification, KTH and Weizmann data sets are used. As the experimental verification data set of this article, the shooting posture of football players in the sports event video is recognized, and the accuracy of the action recognition is finally calculated to standardize the football shooting posture. The experimental results show that the Weizmann data set has a higher accuracy rate than the KTH data set and is more suitable for shooting attitude specifications.

scholarly journals Robust Cherry Tomatoes Detection Algorithm in Greenhouse Scene Based on SSD

Agriculture ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 160
Author(s):  
Ting Yuan ◽  
Lin Lv ◽  
Fan Zhang ◽  
Jun Fu ◽  
Jin Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Detection Algorithm ◽  
Input Image ◽  
Detection Methods ◽  
Single Shot ◽  
Image Size ◽  
Growth Difference ◽  
Input Size ◽  
Base Network ◽  
Cherry Tomatoes

The detection of cherry tomatoes in greenhouse scene is of great significance for robotic harvesting. This paper states a method based on deep learning for cherry tomatoes detection to reduce the influence of illumination, growth difference, and occlusion. In view of such greenhouse operating environment and accuracy of deep learning, Single Shot multi-box Detector (SSD) was selected because of its excellent anti-interference ability and self-taught from datasets. The first step is to build datasets containing various conditions in greenhouse. According to the characteristics of cherry tomatoes, the image samples with illumination change, images rotation and noise enhancement were used to expand the datasets. Then training datasets were used to train and construct network model. To study the effect of base network and the input size of networks, one contrast experiment was designed on different base networks of VGG16, MobileNet, Inception V2 networks, and the other contrast experiment was conducted on changing the network input image size of 300 pixels by 300 pixels, 512 pixels by 512 pixels. Through the analysis of the experimental results, it is found that the Inception V2 network is the best base network with the average precision of 98.85% in greenhouse environment. Compared with other detection methods, this method shows substantial improvement in cherry tomatoes detection.

DEEP LEARNING TECHNIQUE - BASED DRONE DETECTION AND TRACKING

2021 ◽  
pp. 10-19
Author(s):  
Xuan Tung Truong
Keyword(s):  
Deep Learning ◽  
Detection Algorithm ◽  
Tracking Algorithm ◽  
Single Shot ◽  
Complex Environments ◽  
Security Risks ◽  
Security Breaches ◽  
Detection And Tracking ◽  
Illegal Activities ◽  
Fine Tune

The usage of small drones/UAVs is becoming increasingly important in recent years. Consequently, there is a rising potential of small drones being misused for illegal activities such as terrorism, smuggling of drugs, etc. posing high-security risks. Hence, tracking and surveillance of drones are essential to prevent security breaches. This paper resolves the problem of detecting small drones in surveillance videos using deep learning algorithms. Single Shot Detector (SSD) object detection algorithm and MobileNet-v2 architecture as the backbone were used for our experiments. The pre-trained model was re-trained on custom drone synthetic dataset by using transfer learning’s fine-tune technique. The results of detecting drone in our experiments were around 90.8%. The combination of drone detection, Dlib correlation tracking algorithm and centroid tracking algorithm effectively detects and tracks the small drone in various complex environments as well as is able to handle multiple target appearances.

scholarly journals Hyperspectral Imaging Combined with Artificial Intelligence in the Early Detection of Esophageal Cancer

Cancers ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 4593
Author(s):  
Cho-Lun Tsai ◽  
Arvind Mukundan ◽  
Chen-Shuan Chung ◽  
Yi-Hsun Chen ◽  
Yao-Kuang Wang ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Deep Learning ◽  
Hyperspectral Imaging ◽  
Detection Method ◽  
Narrow Band ◽  
Identification System ◽  
Single Shot ◽  
Test Results ◽  
Endoscopic Images ◽  
Rgb Images

This study uses hyperspectral imaging (HSI) and a deep learning diagnosis model that can identify the stage of esophageal cancer and mark the locations. This model simulates the spectrum data from the image using an algorithm developed in this study which is combined with deep learning for the classification and diagnosis of esophageal cancer using a single-shot multibox detector (SSD)-based identification system. Some 155 white-light endoscopic images and 153 narrow-band endoscopic images of esophageal cancer were used to evaluate the prediction model. The algorithm took 19 s to predict the results of 308 test images and the accuracy of the test results of the WLI and NBI esophageal cancer was 88 and 91%, respectively, when using the spectral data. Compared with RGB images, the accuracy of the WLI was 83% and the NBI was 86%. In this study, the accuracy of the WLI and NBI was increased by 5%, confirming that the prediction accuracy of the HSI detection method is significantly improved.

scholarly journals A Framework of an Intelligent Education System for Higher Education Based on Deep Learning

2021 ◽  
Vol 16 (07) ◽  
pp. 233
Author(s):  
Xiuli Zhang ◽  
Zhongqiu Cao
Keyword(s):  
Neural Network ◽  
Higher Education ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Education System ◽  
Block Diagram ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
Functional Block Diagram ◽  
Specific Teaching

Intelligent learning platforms and education information application platforms are gaining ground, owing to the wide application of modern technologies such as the Internet of Things, big data analysis, artificial intelligence, and cloud computing. However, the current platforms cannot solve specific teaching problems, and the relevant research mostly focuses on primary and secondary education. Therefore, this paper constructs and analyzes a framework of intelligent education system for higher education based on the deep learning. Firstly, the functional block diagram of the system was built up. Next, a face detection algorithm was proposed based on the multi-task convolutional neural network, a face recognition algorithm was developed based on the improved deep convolutional neural network, and the knowledge learning status of students was tracked based on the memory augmented neural network. Finally, the proposed framework was proved effective and swift through experiments. The research results expand the application scope of the deep learning in education.

scholarly journals Early warning system for drivers’ phone usage with deep learning network

2021 ◽  
Author(s):  
Jixu Hou ◽  
Xiaofeng Xie ◽  
Qian Cai ◽  
Zhengjie Deng ◽  
Houqun Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Monitoring System ◽  
Warning System ◽  
Detection Algorithm ◽  
Single Shot ◽  
Intelligent Monitoring ◽  
Learning Network ◽  
Dangerous Driving ◽  
Deep Learning Network ◽  
Deep Learning Model

Abstract Dangerous driving, e.g., using mobile phone while driving, can result in serious traffic problem and threat to safely. To efficiently alleviate such problem, in this paper, we design a intelligent monitoring system to detect the dangerous behavior in driving. The monitoring system is combined by camera, terminal server, target detection algorithm and voice reminder. Furthermore, we applied an efficiently deep learning model, namely mobilenet combined with single shot multi-box detector (mobilenet-SSD), to identify the behavior of driver. To evaluate the performance of proposed system, we construct a dangerous driving dataset which consists of 6796 images. The experimental results show that the proposed system can achieve accuracy of 99% in 100 testing images. It can be used for real-time monitoring of the driver’s status.

A fast workpiece detection method based on multi-feature fused SSD

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Guoyuan Shi ◽  
Yingjie Zhang ◽  
Manni Zeng
Keyword(s):  
Deep Learning ◽  
Feature Fusion ◽  
Learning Algorithms ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Data Sets ◽  
Single Shot ◽  
Content Type ◽  
Sorting System ◽  
Study Designs

Purpose Workpiece sorting is a key link in industrial production lines. The vision-based workpiece sorting system is non-contact and widely applicable. The detection and recognition of workpieces are the key technologies of the workpiece sorting system. To introduce deep learning algorithms into workpiece detection and improve detection accuracy, this paper aims to propose a workpiece detection algorithm based on the single-shot multi-box detector (SSD). Design/methodology/approach Propose a multi-feature fused SSD network for fast workpiece detection. First, the multi-view CAD rendering images of the workpiece are used as deep learning data sets. Second, the visual geometry group network was trained for workpiece recognition to identify the category of the workpiece. Third, this study designs a multi-level feature fusion method to improve the detection accuracy of SSD (especially for small objects); specifically, a feature fusion module is added, which uses “element-wise sum” and “concatenation operation” to combine the information of shallow features and deep features. Findings Experimental results show that the actual workpiece detection accuracy of the method can reach 96% and the speed can reach 41 frames per second. Compared with the original SSD, the method improves the accuracy by 7% and improves the detection performance of small objects. Originality/value This paper innovatively introduces the SSD detection algorithm into workpiece detection in industrial scenarios and improves it. A feature fusion module has been added to combine the information of shallow features and deep features. The multi-feature fused SSD network proves the feasibility and practicality of introducing deep learning algorithms into workpiece sorting.

scholarly journals Estimation of Wind Turbine Angular Velocity Remotely Found on Video Mining and Convolutional Neural Network

2020 ◽  
Vol 10 (10) ◽  
pp. 3544 ◽  
Author(s):  
Mahdi Bahaghighat ◽  
Qin Xin ◽  
Seyed Ahmad Motamedi ◽  
Morteza Mohammadi Zanjireh ◽  
Antoine Vacavant
Keyword(s):  
Deep Learning ◽  
Angular Velocity ◽  
Wind Turbine ◽  
Intelligent Systems ◽  
Wind Farm ◽  
Vision System ◽  
Detection Algorithm ◽  
Single Shot ◽  
Video Mining ◽  
Other Information

Today, energy issues are more important than ever. Because of the importance of environmental concerns, clean and renewable energies such as wind power have been most welcomed globally, especially in developing countries. Worldwide development of these technologies leads to the use of intelligent systems for monitoring and maintenance purposes. Besides, deep learning as a new area of machine learning is sharply developing. Its strong performance in computer vision problems has conducted us to provide a high accuracy intelligent machine vision system based on deep learning to estimate the wind turbine angular velocity, remotely. This velocity along with other information such as pitch angle and yaw angle can be used to estimate the wind farm energy production. For this purpose, we have used SSD (Single Shot Multi-Box Detector) object detection algorithm and some specific classification methods based on DenseNet, SqueezeNet, ResNet50, and InceptionV3 models. The results indicate that the proposed system can estimate rotational speed with about 99.05 % accuracy.

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