scholarly journals Effective Face Detector Based on YOLOv5 and Superresolution Reconstruction

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Qingqing Xu ◽  
Zhiyu Zhu ◽  
Huilin Ge ◽  
Zheqing Zhang ◽  
Xu Zang
Keyword(s):  
Face Detection ◽  
Learning Algorithm ◽  
Single Shot ◽  
Scale Invariant ◽  
Detection Model ◽  
Deep Learning Algorithm ◽  
Face Scale ◽  
Complex Scenes ◽  
Face Detection And Recognition ◽  
Face Detector

The application of face detection and recognition technology in security monitoring systems has made a huge contribution to public security. Face detection is an essential first step in many face analysis systems. In complex scenes, the accuracy of face detection would be limited because of the missing and false detection of small faces, due to image quality, face scale, light, and other factors. In this paper, a two-level face detection model called SR-YOLOv5 is proposed to address some problems of dense small faces in actual scenarios. The research first optimized the backbone and loss function of YOLOv5, which is aimed at achieving better performance in terms of mean average precision (mAP) and speed. Then, to improve face detection in blurred scenes or low-resolution situations, we integrated image superresolution technology on the detection head. In addition, some representative deep-learning algorithm based on face detection is discussed by grouping them into a few major categories, and the popular face detection benchmarks are enumerated in detail. Finally, the wider face dataset is used to train and test the SR-YOLOv5 model. Compared with multitask convolutional neural network (MTCNN), Contextual Multi-Scale Region-based CNN (CMS-RCNN), Finding Tiny Faces (HR), Single Shot Scale-invariant Face Detector (S3FD), and TinaFace algorithms, it is verified that the proposed model has higher detection precision, which is 0.7%, 0.6%, and 2.9% higher than the top one. SR-YOLOv5 can effectively use face information to accurately detect hard-to-detect face targets in complex scenes.

scholarly journals Deep-Learning-Based Indoor Human Following of Mobile Robot Using Color Feature

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2699 ◽  
Author(s):  
Redhwan Algabri ◽  
Mun-Taek Choi
Keyword(s):  
Deep Learning ◽  
Obstacle Avoidance ◽  
Learning Algorithm ◽  
Human Robot Interaction ◽  
Target Person ◽  
Single Shot ◽  
Color Feature ◽  
Mapping Algorithm ◽  
Deep Learning Algorithm ◽  
Illumination Changes

Human following is one of the fundamental functions in human–robot interaction for mobile robots. This paper shows a novel framework with state-machine control in which the robot tracks the target person in occlusion and illumination changes, as well as navigates with obstacle avoidance while following the target to the destination. People are detected and tracked using a deep learning algorithm, called Single Shot MultiBox Detector, and the target person is identified by extracting the color feature using the hue-saturation-value histogram. The robot follows the target safely to the destination using a simultaneous localization and mapping algorithm with the LIDAR sensor for obstacle avoidance. We performed intensive experiments on our human following approach in an indoor environment with multiple people and moderate illumination changes. Experimental results indicated that the robot followed the target well to the destination, showing the effectiveness and practicability of our proposed system in the given environment.

scholarly journals S^3FD: Single Shot Scale-Invariant Face Detector

2017 ◽  
Author(s):  
Shifeng Zhang ◽  
Xiangyu Zhu ◽  
Zhen Lei ◽  
Hailin Shi ◽  
Xiaobo Wang ◽  
...  
Keyword(s):  
Single Shot ◽  
Scale Invariant ◽  
Face Detector

scholarly journals Classification of Shellfish Recognition Based on Improved Faster R-CNN Framework of Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yiran Feng ◽  
Xueheng Tao ◽  
Eung-Joo Lee
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Experimental Tests ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Multiple Objects ◽  
Detection Model ◽  
Deep Learning Algorithm ◽  
Quality Sorting

In view of the current absence of any deep learning algorithm for shellfish identification in real contexts, an improved Faster R-CNN-based detection algorithm is proposed in this paper. It achieves multiobject recognition and localization through a second-order detection network and replaces the original feature extraction module with DenseNet, which can fuse multilevel feature information, increase network depth, and avoid the disappearance of network gradients. Meanwhile, the proposal merging strategy is improved with Soft-NMS, where an attenuation function is designed to replace the conventional NMS algorithm, thereby avoiding missed detection of adjacent or overlapping objects and enhancing the network detection accuracy under multiple objects. By constructing a real contexts shellfish dataset and conducting experimental tests on a vision recognition seafood sorting robot production line, we were able to detect the features of shellfish in different scenarios, and the detection accuracy was improved by nearly 4% compared to the original detection model, achieving a better detection accuracy. This provides favorable technical support for future quality sorting of seafood using the improved Faster R-CNN-based approach.

scholarly journals Effective Deep Learning Based Architecture for Pedestrian Detection from Digital Images

2020 ◽  
Vol 9 (3) ◽  
pp. 1498-1508
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Object Detection ◽  
Learning Algorithm ◽  
Pedestrian Detection ◽  
Single Shot ◽  
Deep Learning Algorithm ◽  
Learning Techniques ◽  
Rgb Images ◽  
Deep Convolution Network

This paper is to present an efficient and fast deep learning algorithm based on neural networks for object detection and pedestrian detection. The technique, called MobileNet Single Shot Detector, is an extension to Convolution Neural Networks. This technique is based on depth-wise distinguishable convolutions in order to build a lightweighted deep convolution network. A single filter is applied to each input and outputs are combined by using pointwise convolution. Single Shot Multibox Detector is a feed forward convolution network that is combined with MobileNets to give efficient and accurate results. MobileNets combined with SSD and Multibox Technique makes it much faster than SSD alone can work. The accuracy for this technique is calculated over colored (RGB images) and also on infrared images and its results are compared with the results of shallow machine learning based feature extraction plus classification technique viz. HOG plus SVM technique. The comparison of performance between proposed deep learning and shallow learning techniques has been conducted over benchmark dataset and validation testing over own dataset in order measure efficiency of both algorithms and find an effective algorithm that can work with speed and accurately to be applied for object detection in real world pedestrian detection application.

scholarly journals Detecting Facial Region and Landmarks at Once via Deep Network

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5360
Author(s):  
Taehyung Kim ◽  
Jiwon Mok ◽  
Euichul Lee
Keyword(s):  
Face Detection ◽  
Detection Method ◽  
Single Shot ◽  
Landmark Detection ◽  
Facial Landmark Detection ◽  
Facial Region ◽  
Facial Landmarks ◽  
Detection Model ◽  
Facial Landmark ◽  
Shot Detection

For accurate and fast detection of facial landmarks, we propose a new facial landmark detection method. Previous facial landmark detection models generally perform a face detection step before landmark detection. This greatly affects landmark detection performance depending on which face detection model is used. Therefore, we propose a model that can simultaneously detect a face region and a landmark without performing the face detection step before landmark detection. The proposed single-shot detection model is based on the framework of YOLOv3, a one-stage object detection method, and the loss function and structure are altered to learn faces and landmarks at the same time. In addition, EfficientNet-B0 was utilized as the backbone network to increase processing speed and accuracy. The learned database used 300W-LP with 64 facial landmarks. The average normalized error of the proposed model was 2.32 pixels. The processing time per frame was about 15 milliseconds, and the average precision of face detection was about 99%. As a result of the evaluation, it was confirmed that the single-shot detection model has better performance and speed than the previous methods. In addition, as a result of using the COFW database, which has 29 landmarks instead of 64 to verify the proposed method, the average normalization error was 2.56 pixels, which was also confirmed to show promising performance.

scholarly journals Pattern Detection Model Using a Deep Learning Algorithm for Power Data Analysis in Abnormal Conditions

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1140
Author(s):  
Jeong-Hee Lee ◽  
Jongseok Kang ◽  
We Shim ◽  
Hyun-Sang Chung ◽  
Tae-Eung Sung
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Pattern Detection ◽  
Prediction Algorithm ◽  
Data Set ◽  
Detection Model ◽  
Deep Learning Algorithm ◽  
Abnormal Pattern

Building a pattern detection model using a deep learning algorithm for data collected from manufacturing sites is an effective way for to perform decision-making and assess business feasibility for enterprises, by providing the results and implications of the patterns analysis of big data occurring at manufacturing sites. To identify the threshold of the abnormal pattern requires collaboration between data analysts and manufacturing process experts, but it is practically difficult and time-consuming. This paper suggests how to derive the threshold setting of the abnormal pattern without manual labelling by process experts, and offers a prediction algorithm to predict the potentials of future failures in advance by using the hybrid Convolutional Neural Networks (CNN)–Long Short-Term Memory (LSTM) algorithm, and the Fast Fourier Transform (FFT) technique. We found that it is easier to detect abnormal patterns that cannot be found in the existing time domain after preprocessing the data set through FFT. Our study shows that both train loss and test loss were well developed, with near zero convergence with the lowest loss rate compared to existing models such as LSTM. Our proposition for the model and our method of preprocessing the data greatly helps in understanding the abnormal pattern of unlabeled big data produced at the manufacturing site, and can be a strong foundation for detecting the threshold of the abnormal pattern of big data occurring at manufacturing sites.

scholarly journals Optimizing the Experimental Method for Stomata-Profiling Automation of Soybean Leaves Based on Deep Learning

Plants ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 2714
Author(s):  
Syada Nizer Sultana ◽  
Halim Park ◽  
Sung Hoon Choi ◽  
Hyun Jo ◽  
Jong Tae Song ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Imaging Data ◽  
Web Based ◽  
Detection Model ◽  
Deep Learning Algorithm ◽  
Soybean Leaves ◽  
Deep Learning Model ◽  
Clearing Method ◽  
Good Substitute

Stomatal observation and automatic stomatal detection are useful analyses of stomata for taxonomic, biological, physiological, and eco-physiological studies. We present a new clearing method for improved microscopic imaging of stomata in soybean followed by automated stomatal detection by deep learning. We tested eight clearing agent formulations based upon different ethanol and sodium hypochlorite (NaOCl) concentrations in order to improve the transparency in leaves. An optimal formulation—a 1:1 (v/v) mixture of 95% ethanol and NaOCl (6–14%)—produced better quality images of soybean stomata. Additionally, we evaluated fixatives and dehydrating agents and selected absolute ethanol for both fixation and dehydration. This is a good substitute for formaldehyde, which is more toxic to handle. Using imaging data from this clearing method, we developed an automatic stomatal detector using deep learning and improved a deep-learning algorithm that automatically analyzes stomata through an object detection model using YOLO. The YOLO deep-learning model successfully recognized stomata with high mAP (~0.99). A web-based interface is provided to apply the model of stomatal detection for any soybean data that makes use of the new clearing protocol.

scholarly journals Pill Detection Model for Medicine Inspection Based on Deep Learning

Chemosensors ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 4
Author(s):  
Hyuk-Ju Kwon ◽  
Hwi-Gang Kim ◽  
Sung-Hak Lee
Keyword(s):  
Deep Learning ◽  
Human Error ◽  
Learning Algorithm ◽  
Expansion Method ◽  
Training Data ◽  
Data Set ◽  
Learning Stage ◽  
Detection Model ◽  
Deep Learning Algorithm ◽  
Single Pill

This paper proposes a deep learning algorithm that can improve pill identification performance using limited training data. In general, when individual pills are detected in multiple pill images, the algorithm uses multiple pill images from the learning stage. However, when there is an increase in the number of pill types to be identified, the pill combinations in an image increase exponentially. To detect individual pills in an image that contains multiple pills, we first propose an effective database expansion method for a single pill. Then, the expanded training data are used to improve the detection performance. Our proposed method shows higher performance improvement than the existing algorithms despite the limited imaging and data set size. Our proposed method will help minimize problems, such as loss of productivity and human error, which occur while inspecting dispensed pills.

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