scholarly journals Real-Time Human Ear Detection Based on the Joint of Yolo and RetinaFace

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Huy Nguyen Quoc ◽  
Vinh Truong Hoang
Keyword(s):  
Real Time ◽  
Detection System ◽  
Real Life ◽  
Identification System ◽  
Detection Algorithms ◽  
Ear Detection ◽  
Unique Shape ◽  
Human Ear ◽  
Real Time Application ◽  
Face Detector

Biometric traits gradually proved their importance in real-life applications, especially in identification field. Among the available biometric traits, the unique shape of the human ear has also received loads of attention from scientists through the years. Hence, numerous ear-based approaches have been proposed with promising performance. With these methods, plenty problems can be solve by the distinctiveness of ear features, such as recognizing human with mask or diagnose ear-related diseases. As a complete identification system requires an effective detector for real-time application, and the current richness and variety of ear detection algorithms are poor due to the small and complex shape of human ears. In this paper, we introduce a new human ear detection pipeline based on the YOLOv3 detector. A well-known face detector named RetinaFace is also added in the detection system to narrow the regions of interest and enhance the accuracy. The proposed method is evaluated on an unconstrained dataset, which shows its effectiveness.

scholarly journals ANALYSIS OF HUMAN FACE RECOGNITION ALGORITHM USING PCA+FDIT IN IMAGE DATABASE FOR CRIME INVESTIGATION

2013 ◽  
Vol 4 (3) ◽  
pp. 788-796
Author(s):  
V. S. Manjula
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Face Detection ◽  
Skin Color ◽  
Back Propagation ◽  
Real Life ◽  
Hierarchical Architecture ◽  
Detection And Identification ◽  
The Face ◽  
Real Time Application

In general, the field of face recognition has lots of research that have put interest in order to detect the face and to identify it and also to track it. Many researchers have concentrated on the face identification and detection problem by using various approaches. The proposed approach is further very useful and helpful in real time application. Thus the Face Detection, Identification  which is proposed here is used to detect the faces in videos in the real time application by using the FDIT (Face Detection Identification Technique) algorithm. Thus the proposed mechanism is very help full in identifying individual persons who are been involved in the action of robbery, murder cases and terror activities. Although in face recognition the algorithm used is of histogram equalization combined with Back propagation neural network in which we recognize an unknown test image by comparing it with the known training set images that are been stored in the database. Also the proposed approach uses skin color extraction as a parameter for face detection. A multi linear training and rectangular face feature extraction are done for training, identifying and detecting.   Thus the proposed technique   is PCA + FDIT technique configuration only improved recognition for subjects in images are included in the training data.   It is very useful in identify a single person from a group of faces.   Thus the proposed technique is well suited for all kinds faces frame work for face detection and identification. The face detection and identification modules share the same hierarchical architecture. They both consist of two layers of classifiers, a layer with a set of component classifiers and a layer with a single combination classifier.  Also we have taken a real life example and simulated the algorithms in IDL Tool successfully.

scholarly journals Development of a Real-Time Detection System for Augmented Reality Driving

2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
Author(s):  
Kuei-Shu Hsu ◽  
Chia-Sui Wang ◽  
Jinn-Feng Jiang ◽  
Hung-Yuan Wei
Keyword(s):  
Virtual Reality ◽  
Augmented Reality ◽  
Real Time ◽  
Detection System ◽  
Real Life ◽  
Error Rates ◽  
Interactive Effects ◽  
Gaming Technology ◽  
Augmented Reality Technology ◽  
Real Scenario

Augmented reality technology is applied so that driving tests may be performed in various environments using a virtual reality scenario with the ultimate goal of improving visual and interactive effects of simulated drivers. Environmental conditions simulating a real scenario are created using an augmented reality structure, which guarantees the test taker’s security since they are not subject to real-life elements and dangers. Furthermore, the accuracy of tests conducted through virtual reality is not influenced by either environmental or human factors. Driver posture is captured in real time using Kinect’s depth perception function and then applied to driving simulation effects that are emulated by Unity3D’s gaming technology. Subsequently, different driving models may be collected through different drivers. In this research, nearly true and realistic street environments are simulated to evaluate driver behavior. A variety of different visual effects are easily available to effectively reduce error rates, thereby significantly improving test security as well as the reliability and reality of this project. Different situation designs are simulated and evaluated to increase development efficiency and build more security verification test platforms using such technology in conjunction with driving tests, vehicle fittings, environmental factors, and so forth.

scholarly journals Severity Prediction System for Real Time Pothole Detection

2021 ◽  
Vol 23 (07) ◽  
pp. 1328-1334
Author(s):  
Sumit Bhimte ◽  
◽  
Hrishikesh hasabnis ◽  
Rohit Shirsath ◽  
Saurabh Sonar ◽  
...  
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Detection System ◽  
Detection Algorithm ◽  
Prediction System ◽  
Image Processing Algorithm ◽  
Efficient Technology ◽  
Severity Prediction ◽  
Pothole Detection ◽  
Real Time Application

Pothole Detection System using Image Processing or using Accelerometer is not a new normal. But there is no real time application which utilizes both techniques to provide us with efficient solution. We present a system which can be useful for the drivers to determine the intensity of Pothole using both Image Processing Technology and Accelerometer device-based Algorithm. The challenge in building this system was to efficiently detect a Pothole present in roads, to analyze the severity of Pothole and to provide users with information like Road Quality and best possible route. We have used various algorithms for frequency-based pothole detection. We compared the results. Apart from that, we selected the best approach suitable for achieving the project goals. We have used a Simple Differentiation-based Edge Detection Algorithm for Image Processing. The system has been built on Map Interfaces for Android devices using Android Studio, which consists of usage of Image Processing Algorithm based Python frameworks which is a sub field of Machine Learning. It is backed by powerful DBMS. This project facilitates use of most efficient technology tools to provide a good user experience, real time application, reliability and improved efficiency.

scholarly journals Multi Comm_Plus: A Community Detection System for Identification of Community in Multi-Dimensional Networks

2016 ◽  
Vol 4 (05) ◽  
pp. 9879-9884
Author(s):  
Dhanya Sudhakaran ◽  
Shini Renjith
Keyword(s):  
Big Data ◽  
Real Time ◽  
Community Detection ◽  
Data Analytics ◽  
Selection Criteria ◽  
Large Scale ◽  
Detection System ◽  
Big Data Analytics ◽  
Detection Algorithms ◽  
Large Scale Networks

Community detection is a common problem in graph and big data analytics. It consists of finding groups of densely connected nodes with few connections to nodes outside of the group. In particular, identifying communities in large-scale networks is an important task in many scientific domains. Community detection algorithms in literature proves to be less efficient, as it leads to generation of communities with noisy interactions. To address this limitation, there is a need to develop a system which identifies the best community among multi-dimensional networks based on relevant selection criteria and dimensionality of entities, thereby eliminating the noisy interactions in a real-time environment.

scholarly journals A Real-Time Detection Algorithm for Kiwifruit Defects Based on YOLOv5

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1711
Author(s):  
Jia Yao ◽  
Jiaming Qi ◽  
Jie Zhang ◽  
Hongmin Shao ◽  
Jia Yang ◽  
...  
Keyword(s):  
Real Time ◽  
Defect Detection ◽  
Detection System ◽  
Flaw Detection ◽  
Detection Algorithm ◽  
Small Object ◽  
Original Algorithm ◽  
Detection Algorithms ◽  
Model Based ◽  
Real Time Detection

Defect detection is the most important step in the postpartum reprocessing of kiwifruit. However, there are some small defects difficult to detect. The accuracy and speed of existing detection algorithms are difficult to meet the requirements of real-time detection. For solving these problems, we developed a defect detection model based on YOLOv5, which is able to detect defects accurately and at a fast speed. The main contributions of this research are as follows: (1) a small object detection layer is added to improve the model’s ability to detect small defects; (2) we pay attention to the importance of different channels by embedding SELayer; (3) the loss function CIoU is introduced to make the regression more accurate; (4) under the prerequisite of no increase in training cost, we train our model based on transfer learning and use the CosineAnnealing algorithm to improve the effect. The results of the experiment show that the overall performance of the improved network YOLOv5-Ours is better than the original and mainstream detection algorithms. The [email protected] of YOLOv5-Ours has reached 94.7%, which was an improvement of nearly 9%, compared to the original algorithm. Our model only takes 0.1 s to detect a single image, which proves the effectiveness of the model. Therefore, YOLOv5-Ours can well meet the requirements of real-time detection and provides a robust strategy for the kiwi flaw detection system.

Ambulatory Assessment

2009 ◽  
Vol 14 (2) ◽  
pp. 109-119 ◽  
Author(s):  
Ulrich W. Ebner-Priemer ◽  
Timothy J. Trull
Keyword(s):  
Real Time ◽  
Clinical Psychology ◽  
Real Life ◽  
Ambulatory Assessment ◽  
Computer Assisted ◽  
Affective Valence ◽  
Time Data ◽  
Physiological Processes ◽  
Context Specific ◽  
Questionnaire Studies

Convergent experimental data, autobiographical studies, and investigations on daily life have all demonstrated that gathering information retrospectively is a highly dubious methodology. Retrospection is subject to multiple systematic distortions (i.e., affective valence effect, mood congruent memory effect, duration neglect; peak end rule) as it is based on (often biased) storage and recollection of memories of the original experience or the behavior that are of interest. The method of choice to circumvent these biases is the use of electronic diaries to collect self-reported symptoms, behaviors, or physiological processes in real time. Different terms have been used for this kind of methodology: ambulatory assessment, ecological momentary assessment, experience sampling method, and real-time data capture. Even though the terms differ, they have in common the use of computer-assisted methodology to assess self-reported symptoms, behaviors, or physiological processes, while the participant undergoes normal daily activities. In this review we discuss the main features and advantages of ambulatory assessment regarding clinical psychology and psychiatry: (a) the use of realtime assessment to circumvent biased recollection, (b) assessment in real life to enhance generalizability, (c) repeated assessment to investigate within person processes, (d) multimodal assessment, including psychological, physiological and behavioral data, (e) the opportunity to assess and investigate context-specific relationships, and (f) the possibility of giving feedback in real time. Using prototypic examples from the literature of clinical psychology and psychiatry, we demonstrate that ambulatory assessment can answer specific research questions better than laboratory or questionnaire studies.

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