Analysis of Classroom Teaching Status Based on Target Detection Model

2019 ◽  
Author(s):  
Biao Wang ◽  
Xiao Guo
Keyword(s):  
Target Detection ◽  
Classroom Teaching ◽  
Detection Model ◽  
Teaching Status

scholarly journals Research on Lightweight Infrared Pedestrian Detection Model Algorithm for Embedded Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Zhaoli Wu ◽  
Xin Wang ◽  
Chao Chen
Keyword(s):  
Real Time ◽  
Target Detection ◽  
Pedestrian Detection ◽  
Infrared Image ◽  
Far Infrared ◽  
Detection Algorithm ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Detection Model ◽  
Embedded Platform

Due to the limitation of energy consumption and power consumption, the embedded platform cannot meet the real-time requirements of the far-infrared image pedestrian detection algorithm. To solve this problem, this paper proposes a new real-time infrared pedestrian detection algorithm (RepVGG-YOLOv4, Rep-YOLO), which uses RepVGG to reconstruct the YOLOv4 backbone network, reduces the amount of model parameters and calculations, and improves the speed of target detection; using space spatial pyramid pooling (SPP) obtains different receptive field information to improve the accuracy of model detection; using the channel pruning compression method reduces redundant parameters, model size, and computational complexity. The experimental results show that compared with the YOLOv4 target detection algorithm, the Rep-YOLO algorithm reduces the model volume by 90%, the floating-point calculation is reduced by 93.4%, the reasoning speed is increased by 4 times, and the model detection accuracy after compression reaches 93.25%.

scholarly journals Bacterial colonies detecting and counting based on enhanced CNN detection method

2021 ◽  
Vol 233 ◽  
pp. 02012
Author(s):  
Shousheng Liu ◽  
Zhigang Gai ◽  
Xu Chai ◽  
Fengxiang Guo ◽  
Mei Zhang ◽  
...  
Keyword(s):  
Target Detection ◽  
Error Detection ◽  
Detection Rate ◽  
Detection Method ◽  
Difficult Problem ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Detection Model ◽  
Confidence Threshold ◽  
Small Targets

Bacterial colonies detecting and counting is tedious and time-consuming work. Fortunately CNN (convolutional neural network) detection methods are effective for target detection. The bacterial colonies are a kind of small targets, which have been a difficult problem in the field of target detection technology. This paper proposes a small target enhancement detection method based on double CNNs, which can not only improve the detection accuracy, but also maintain the detection speed similar to the general detection model. The detection method uses double CNNs. The first CNN uses SSD_MOBILENET_V1 network with both target positioning and target recognition functions. The candidate targets are screened out with a low confidence threshold, which can ensure no missing detection of small targets. The second CNN obtains candidate target regions according to the first round of detection, intercepts image sub-blocks one by one, uses the MOBILENET_V1 network to filter out targets with a higher confidence threshold, which can ensure good detection of small targets. Through the two-round enhancement detection method has been transplanted to the embedded platform NVIDIA Jetson AGX Xavier, the detection accuracy of small targets is significantly improved, and the target error detection rate and missed detection rate are reduced to less than 1%.

scholarly journals DETECTION OF SHIP TARGETS IN POLARIMETRIC SAR DATA USING 2D-PCA DATA FUSION

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1017-1024
Author(s):  
C. Theoharatos ◽  
A. Makedonas ◽  
N. Fragoulis ◽  
V. Tsagaris ◽  
S. Costicoglou
Keyword(s):  
Data Fusion ◽  
Target Detection ◽  
Single Channel ◽  
False Alarms ◽  
Detection Accuracy ◽  
Constant False Alarm Rate ◽  
Additional Advantage ◽  
Detection Model ◽  
Background Clutter ◽  
Fusion Scheme

Data fusion has lately received a lot of attention as an effective technique for several target detection and classification applications in different remote sensing areas. In this work, a novel data fusion scheme for improving the detection accuracy of ship targets in polarimetric data is proposed, based on 2D principal components analysis (2D-PCA) technique. By constructing a fused image from different polarization channels, increased performance of ship target detection is achieved having higher true positive and lower false positive detection accuracy as compared to single channel detection performance. In addition, the use of 2D-PCA provides the ability to discriminate and classify objects and regions in the resulting image representation more effectively, with the additional advantage of being more computational efficient and requiring less time to determine the corresponding eigenvectors, compared to e.g. conventional PCA. Throughout our analysis, a constant false alarm rate (CFAR) detection model is applied to characterize the background clutter and discriminate ship targets based on the Weibull distribution and the calculation of local statistical moments for estimating the order statistics of the background clutter. Appropriate pre-processing and post-processing techniques are also introduced to the process chain, in order to boost ship discrimination and suppress false alarms caused by range focusing artifacts. Experimental results provided on a set of Envisat and RadarSat-2 images (dual and quad polarized respectively), demonstrate the advantage of the proposed data fusion scheme in terms of detection accuracy as opposed to single data ship detection and conventional PCA, in various sea conditions and resolutions. Further investigation of other data fusion techniques is currently in progress.

Research on the Passive Detection Model Based on 3D-HBF and FSVDD for Underwater High-Speed Small Targets and its Application

2011 ◽  
Vol 317-319 ◽  
pp. 1282-1288
Author(s):  
Qiao Hu ◽  
Bao An Hao ◽  
Hong Yi ◽  
Yun Chuan Yang
Keyword(s):  
Target Detection ◽  
High Speed ◽  
Beam Width ◽  
Target Strength ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Passive Detection ◽  
Detection Model ◽  
Model Based ◽  
Small Targets

Due to the high-speed, short-time countermeasure and small target strength of underwater high-speed small targets (UHSST), it is difficult to use a traditional method to accurately detect UHSST. So a novel passive detection model based on three-dimensional hyperbeam forming (3D-HBF) and fuzzy support vector data description (FSVDD) is proposed, where these advantages of beam width reduction and side lobe suppression for 3D-HBF and excellent target-detection capability for FSVDD are combined. The model consists of two stages. In the first stage, 3D-HBF is carried out to obtain the beam respond vectors (BSV) from original underwater acoustic signals. In the second stage, the BSV are input into the detector based on FSVDD to detect and locate the underwater targets intelligently. This model is applied to target detection of UHSST, and these testing results show that the proposed model has better detection performance than the conventional beam forming method, with a high detection success rate and localization capability.

Teaching Reform and Practice of Electrical Courses

2011 ◽  
Vol 271-273 ◽  
pp. 1860-1863
Author(s):  
Hong Bo Wei
Keyword(s):  
Teaching Quality ◽  
Classroom Teaching ◽  
Engineering Practice ◽  
Positive Role ◽  
Positive Effects ◽  
Teaching Reform ◽  
Teaching Status ◽  
And Training

In order to improve the effects of classroom teaching and experiment teaching, a viewpoint that combines the software with hardware is proposed based on the analysis of the characteristics and teaching status in electrical courses. Through practise it has been proved that this method has played a positive role in increasing students’ interest and enhencing practical and innovation abilities. At the same time, it has positive effects in improving teaching quality and training the students’ engineering practice ability .

A Statistical Model for Real-Time Video Moving Target Detection Based on Bayesian Statistics

2014 ◽  
Vol 596 ◽  
pp. 394-397 ◽  
Author(s):  
Zhi Hong Zhang ◽  
Shu Ling Zhang ◽  
Bin Yang ◽  
Xin Bai
Keyword(s):  
Statistical Model ◽  
Real Time ◽  
Target Detection ◽  
Moving Objects ◽  
Relevant Information ◽  
Moving Target ◽  
Moving Target Detection ◽  
Detection Model ◽  
Advantages And Disadvantages ◽  
Time Space

Video moving target detection is an important foundation issues in computer vision, based on the analysis of the advantages and disadvantages of each existing moving target detection model, using Bayesian statistical theory as a framework, proposes a statistical model that can detect moving objects in video in real-time. The model combines time, space and color and other relevant information of pixel, divides and extracts Video segmentation’s foreground. By selecting the appropriate reference background can improve the precision and accuracy of the detection.

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