scholarly journals DWCA-YOLOv5: An Improve Single Shot Detector for Safety Helmet Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhang Jin ◽  
Peiqi Qu ◽  
Cheng Sun ◽  
Meng Luo ◽  
Yan Gui ◽  
...  
Keyword(s):  
Information Dissemination ◽  
Detection Efficiency ◽  
A Priori ◽  
Detection Methods ◽  
Single Shot ◽  
Accuracy Rate ◽  
Average Accuracy ◽  
Head Detection ◽  
Matching Degree ◽  
Complex Construction

Aiming at solving the problem that the detection methods used in the existing helmet detection research has low detection efficiency and the cumulative error influences accuracy, a new algorithm for improving YOLOv5 helmet wearing detection is proposed. First of all, we use the K -means++ algorithm to improve the size matching degree of the a priori anchor box; secondly, integrate the Depthwise Coordinate Attention (DWCA) mechanism in the backbone network, so that the network can learn the weight of each channel independently and enhance the information dissemination between features, thereby strengthening the network’s ability to distinguish foreground and background. The experimental results show as follows: in the self-made safety helmet wearing detection dataset, the average accuracy rate reached 95.9%, the average accuracy of the helmet detection reached 96.5%, and the average accuracy of the worker’s head detection reached 95.2%. Making a comparison with the YOLOv5 algorithm, our model has a 3% increase in the average accuracy of helmet detection, which is in line with the accuracy requirements of helmet wearing detection in complex construction scenarios.

Detection of Hands for Hand-Controlled Skyfall Game in Real Time Using CNN

2020 ◽  
Vol 10 (2) ◽  
pp. 15-25
Author(s):  
Neha B. ◽  
Naveen V. ◽  
Angelin Gladston
Keyword(s):  
Real Time ◽  
Detection Methods ◽  
Input Device ◽  
Single Shot ◽  
Hand Detection ◽  
Web Based ◽  
Fast Detection ◽  
Detection And Tracking ◽  
Average Accuracy ◽  
Direct Use

With human-computer interaction technology evolving, direct use of the hand as an input device is of wide attraction. Recently, object detection methods using CNN models have significantly improved the accuracy of hand detection. This paper focuses on creating a hand-controlled web-based skyfall game by building a real time hand detection using CNN-based technique. A CNN network, which uses a MobileNet as the feature extractor along with the single shot detector framework, is used to achieve a robust and fast detection of hand location and tracking. Along with detection and tracking of hand, skyfall game has been designed to play using hand in real time with tensor flow framework. This way of designing the game where hand is used as input to control the paddle of skyfall game improved the player interaction and interest towards playing the game. This model of CNN network used egohands dataset for detecting and tracking the hands in real time and produced an average accuracy of 0.9 for open hands and 0.6 for closed hands which in turn improved player and game interactions.

scholarly journals NSD-SSD: A Novel Real-Time Ship Detector Based on Convolutional Neural Network in Surveillance Video

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Jiuwu Sun ◽  
Zhijing Xu ◽  
Shanshan Liang
Keyword(s):  
Real Time ◽  
Clustering Algorithm ◽  
Detection Efficiency ◽  
Rapid Development ◽  
Outdoor Environment ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Single Shot ◽  
Ship Detection ◽  
Real Time Detection

With the rapid development of the marine industry, intelligent ship detection plays a very important role in the marine traffic safety and the port management. Current detection methods mainly focus on synthetic aperture radar (SAR) images, which is of great significance to the field of ship detection. However, these methods sometimes cannot meet the real-time requirement. To solve the problems, a novel ship detection network based on SSD (Single Shot Detector), named NSD-SSD, is proposed in this paper. Nowadays, the surveillance system is widely used in the indoor and outdoor environment, and its combination with deep learning greatly promotes the development of intelligent object detection and recognition. The NSD-SSD uses visual images captured by surveillance cameras to achieve real-time detection and further improves detection performance. First, dilated convolution and multiscale feature fusion are combined to improve the small objects’ performance and detection accuracy. Second, an improved prediction module is introduced to enhance deeper feature extraction ability of the model, and the mean Average Precision (mAP) and recall are significant improved. Finally, the prior boxes are reconstructed by using the K-means clustering algorithm, the Intersection-over-Union (IoU) is higher, and the visual effect is better. The experimental results based on ship images show that the mAP and recall can reach 89.3% and 93.6%, respectively, which outperforms the representative model (Faster R-CNN, SSD, and YOLOv3). Moreover, our model’s FPS is 45, which can meet real-time detection acquirement well. Hence, the proposed method has the better overall performance and achieves higher detection efficiency and better robustness.

scholarly journals Building Damage Detection from Post-Event Aerial Imagery Using Single Shot Multibox Detector

2019 ◽  
Vol 9 (6) ◽  
pp. 1128 ◽  
Author(s):  
Yundong Li ◽  
Wei Hu ◽  
Han Dong ◽  
Xueyan Zhang
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Hurricane Sandy ◽  
Training Data ◽  
Aerial Images ◽  
Detection Methods ◽  
Single Shot ◽  
Data Set ◽  
Augmentation Strategies ◽  
Post Disaster

Using aerial cameras, satellite remote sensing or unmanned aerial vehicles (UAV) equipped with cameras can facilitate search and rescue tasks after disasters. The traditional manual interpretation of huge aerial images is inefficient and could be replaced by machine learning-based methods combined with image processing techniques. Given the development of machine learning, researchers find that convolutional neural networks can effectively extract features from images. Some target detection methods based on deep learning, such as the single-shot multibox detector (SSD) algorithm, can achieve better results than traditional methods. However, the impressive performance of machine learning-based methods results from the numerous labeled samples. Given the complexity of post-disaster scenarios, obtaining many samples in the aftermath of disasters is difficult. To address this issue, a damaged building assessment method using SSD with pretraining and data augmentation is proposed in the current study and highlights the following aspects. (1) Objects can be detected and classified into undamaged buildings, damaged buildings, and ruins. (2) A convolution auto-encoder (CAE) that consists of VGG16 is constructed and trained using unlabeled post-disaster images. As a transfer learning strategy, the weights of the SSD model are initialized using the weights of the CAE counterpart. (3) Data augmentation strategies, such as image mirroring, rotation, Gaussian blur, and Gaussian noise processing, are utilized to augment the training data set. As a case study, aerial images of Hurricane Sandy in 2012 were maximized to validate the proposed method’s effectiveness. Experiments show that the pretraining strategy can improve of 10% in terms of overall accuracy compared with the SSD trained from scratch. These experiments also demonstrate that using data augmentation strategies can improve mAP and mF1 by 72% and 20%, respectively. Finally, the experiment is further verified by another dataset of Hurricane Irma, and it is concluded that the paper method is feasible.

scholarly journals Robust and fast post-processing of single-shot spin qubit detection events with a neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tom Struck ◽  
Javed Lindner ◽  
Arne Hollmann ◽  
Floyd Schauer ◽  
Andreas Schmidbauer ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bayesian Inference ◽  
Rabi Oscillation ◽  
Detection Methods ◽  
Measured Signal ◽  
Single Shot ◽  
Post Processing ◽  
Readout Signal ◽  
Spin Qubit

AbstractEstablishing low-error and fast detection methods for qubit readout is crucial for efficient quantum error correction. Here, we test neural networks to classify a collection of single-shot spin detection events, which are the readout signal of our qubit measurements. This readout signal contains a stochastic peak, for which a Bayesian inference filter including Gaussian noise is theoretically optimal. Hence, we benchmark our neural networks trained by various strategies versus this latter algorithm. Training of the network with 106 experimentally recorded single-shot readout traces does not improve the post-processing performance. A network trained by synthetically generated measurement traces performs similar in terms of the detection error and the post-processing speed compared to the Bayesian inference filter. This neural network turns out to be more robust to fluctuations in the signal offset, length and delay as well as in the signal-to-noise ratio. Notably, we find an increase of 7% in the visibility of the Rabi oscillation when we employ a network trained by synthetic readout traces combined with measured signal noise of our setup. Our contribution thus represents an example of the beneficial role which software and hardware implementation of neural networks may play in scalable spin qubit processor architectures.

scholarly journals Identification of Skin Lesions by Using Single-Step Multiframe Detector

2021 ◽  
Vol 10 (1) ◽  
pp. 144
Author(s):  
Yu-Ping Hsiao ◽  
Chih-Wei Chiu ◽  
Chih-Wei Lu ◽  
Hong Thai Nguyen ◽  
Yu Sheng Tseng ◽  
...  
Keyword(s):  
Ground Truth ◽  
Skin Lesions ◽  
Single Step ◽  
Single Shot ◽  
Accuracy Rate ◽  
Tissue Slices ◽  
Image Detection ◽  
Sensitivity Rate ◽  
Diagnosis Accuracy ◽  
Artificial Intelligence Algorithm

An artificial intelligence algorithm to detect mycosis fungoides (MF), psoriasis (PSO), and atopic dermatitis (AD) is demonstrated. Results showed that 10 s was consumed by the single shot multibox detector (SSD) model to analyze 292 test images, among which 273 images were correctly detected. Verification of ground truth samples of this research come from pathological tissue slices and OCT analysis. The SSD diagnosis accuracy rate was 93%. The sensitivity values of the SSD model in diagnosing the skin lesions according to the symptoms of PSO, AD, MF, and normal were 96%, 80%, 94%, and 95%, and the corresponding precision were 96%, 86%, 98%, and 90%. The highest sensitivity rate was found in MF probably because of the spread of cancer cells in the skin and relatively large lesions of MF. Many differences were found in the accuracy between AD and the other diseases. The collected AD images were all in the elbow or arm and other joints, the area with AD was small, and the features were not obvious. Hence, the proposed SSD could be used to identify the four diseases by using skin image detection, but the diagnosis of AD was relatively poor.

Improved SSD-assisted algorithm for surface defect detection of electromagnetic luminescence

2021 ◽  
pp. 1748006X2199538
Author(s):  
Zhenying Xu ◽  
Ziqian Wu ◽  
Wei Fan
Keyword(s):  
Defect Detection ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Detection Methods ◽  
Small Scale ◽  
Detection Accuracy ◽  
Single Shot ◽  
Surface Defect Detection ◽  
Feature Pyramid ◽  
Small Feature

Defect detection of electromagnetic luminescence (EL) cells is the core step in the production and preparation of solar cell modules to ensure conversion efficiency and long service life of batteries. However, due to the lack of feature extraction capability for small feature defects, the traditional single shot multibox detector (SSD) algorithm performs not well in EL defect detection with high accuracy. Consequently, an improved SSD algorithm with modification in feature fusion in the framework of deep learning is proposed to improve the recognition rate of EL multi-class defects. A dataset containing images with four different types of defects through rotation, denoising, and binarization is established for the EL. The proposed algorithm can greatly improve the detection accuracy of the small-scale defect with the idea of feature pyramid networks. An experimental study on the detection of the EL defects shows the effectiveness of the proposed algorithm. Moreover, a comparison study shows the proposed method outperforms other traditional detection methods, such as the SIFT, Faster R-CNN, and YOLOv3, in detecting the EL defect.

scholarly journals TWC-Net: A SAR Ship Detection Using Two-Way Convolution and Multiscale Feature Mapping

2021 ◽  
Vol 13 (13) ◽  
pp. 2558
Author(s):  
Lei Yu ◽  
Haoyu Wu ◽  
Zhi Zhong ◽  
Liying Zheng ◽  
Qiuyue Deng ◽  
...  
Keyword(s):  
Target Detection ◽  
Detection Methods ◽  
Observation System ◽  
Single Shot ◽  
Small Target ◽  
Feature Mapping ◽  
Sar Images ◽  
Two Stage ◽  
Ship Detection ◽  
Deep Feature

Synthetic aperture radar (SAR) is an active earth observation system with a certain surface penetration capability and can be employed to observations all-day and all-weather. Ship detection using SAR is of great significance to maritime safety and port management. With the wide application of in-depth learning in ordinary images and good results, an increasing number of detection algorithms began entering the field of remote sensing images. SAR image has the characteristics of small targets, high noise, and sparse targets. Two-stage detection methods, such as faster regions with convolution neural network (Faster RCNN), have good results when applied to ship target detection based on the SAR graph, but their efficiency is low and their structure requires many computing resources, so they are not suitable for real-time detection. One-stage target detection methods, such as single shot multibox detector (SSD), make up for the shortage of the two-stage algorithm in speed but lack effective use of information from different layers, so it is not as good as the two-stage algorithm in small target detection. We propose the two-way convolution network (TWC-Net) based on a two-way convolution structure and use multiscale feature mapping to process SAR images. The two-way convolution module can effectively extract the feature from SAR images, and the multiscale mapping module can effectively process shallow and deep feature information. TWC-Net can avoid the loss of small target information during the feature extraction, while guaranteeing good perception of a large target by the deep feature map. We tested the performance of our proposed method using a common SAR ship dataset SSDD. The experimental results show that our proposed method has a higher recall rate and precision, and the F-Measure is 93.32%. It has smaller parameters and memory consumption than other methods and is superior to other methods.

Weak Transient Electromagnetic Radiation Signal Detection Method Considering the New Watershed Image Segmentation Algorithm

2019 ◽  
Vol 34 (03) ◽  
pp. 2054009
Author(s):  
Wenjun Huo ◽  
Peng Chu ◽  
Kai Wang ◽  
Liangting Fu ◽  
Zhigang Niu ◽  
...  
Keyword(s):  
Electromagnetic Radiation ◽  
Cross Correlation ◽  
Detection Efficiency ◽  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Detection Methods ◽  
Signal To Noise ◽  
Long Distance ◽  
Transient Electromagnetic ◽  
Noise Ratio

In order to study the detection methods of weak transient electromagnetic radiation signals, a detection algorithm integrating generalized cross-correlation and chaotic sequence prediction is proposed in this paper. Based on the dual-antenna test and cross-correlation information estimation method, the detection of aperiodic weak discharge signals under low signal-to-noise ratio is transformed into the estimation of periodic delay parameters, and the noise is reduced at the same time. The feasibility of this method is verified by simulation and experimental analysis. The results show that under the condition of low signal-to-noise ratio, the integrated method can effectively suppress the influence of 10 noise disturbances. It has a high detection probability for weak transient electromagnetic radiation signals, and needs fewer pulse accumulation times, which improves the detection efficiency and is more suitable for long-distance detection of weak electromagnetic radiation sources.

A Geometric Model of the Beating Heart

2007 ◽  
Vol 46 (03) ◽  
pp. 282-286 ◽  
Author(s):  
C. Lorenz ◽  
J. von Berg
Keyword(s):  
Computed Tomography ◽  
A Priori ◽  
Anatomical Landmarks ◽  
Motion Model ◽  
A Priori Information ◽  
Average Accuracy ◽  
Tomography Image ◽  
The Mean ◽  
Priori Information ◽  
Computed Tomography Image

Summary Objectives : A comprehensive model of the human heart that covers multiple surfaces, like those of the four chambers and the attached vessels, is presented. It also contains the coronary arteries and a set of 25 anatomical landmarks. The statistical model is intended to provide a priori information for automated diagnostic and interventional procedures. Methods : The end-diastolic phase of the model was adapted to fit 27 clinical multi-slice computed tomography images, thus reflecting the anatomical variability to be observed in that sample. A mean cardiac motion model was also calculated from a set of eleven multi-phase computed tomography image sets. A number of experiments were performed to determine the accuracy of model-based predictions done on unseen cardiac images. Results : Using an additional deformable surface technique, the model allows for determination of all chambers and the attached vessels on the basis of given anatomical landmarks with an average accuracy of 1.1 mm. After such an individualization of the model by surface adaptation the centerlines of the three main coronary arteries may be estimated with an average accuracy of 5.2 mm. The mean motion model was used to estimate the cardiac phase of an unknown multislice computed tomography image. Conclusion : The mean shape model of the human heart as presented here complements automated image analysis methods with the required a priori information about anatomical constraints to make them work fast and robustly.

scholarly journals Optimalisasi Prediksi Biaya Komisi Penjualan Mobil Menggunakan Metode Monte Carlo

KOMTEKINFO ◽  
2020 ◽  
Vol 7 (2) ◽  
pp. 140-151
Author(s):  
Zupri Henra Hartomi ◽  
Yuhandri ◽  
Julius Santony
Keyword(s):  
Decision Making ◽  
Monte Carlo ◽  
Accuracy Rate ◽  
Sales Data ◽  
For Profit ◽  
Marketing Performance ◽  
Potential Market ◽  
Average Accuracy ◽  
Car Sales ◽  
The Cost

Sales are the main source of income for every company. Every company in marketing a product, should control the potential market for profit. Predicting the number of sales is important in analyzing sales progress. This study aims to assist companies in predicting car sales and car commission cost budgets based on sales data from the previous year.The data used in the study are car sales data for 2017 and 2018 in the Arengka Automall Pekanbaru Showroom (SAA Pekanbaru).Data processing in research uses the Monte Carlo method.The results of tests that have been carried out state that car sales by Marketing within 1 year resulted in an average accuracy rate of 94% and sales commission fee of Rp 411.000.000.From these results in accordance with calculations performed manually so that with a large accuracy value, the application of the simulation using this Monte Carlo Method feasible to be applied by companies in future decision making to plan the estimated budget for the cost of a car sales commission and as a means to assess Marketing performance at SAA Pekanbaru.

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