Real-time target detection technology of large view-field infrared image based on multicore DSP parallel processing

2013 ◽  
Author(s):  
Gang Sun ◽  
Songlin Liu ◽  
Weihua Wang ◽  
Zengping Chen
Keyword(s):  
Parallel Processing ◽  
Real Time ◽  
Target Detection ◽  
Infrared Image ◽  
Detection Technology ◽  
Multicore Dsp

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%.

Overview on Small Target Detection Technology in Infrared Image

2016 ◽  
Vol 53 (5) ◽  
pp. 050004
Author(s):  
刘让 Liu Rang ◽  
王德江 Wang Dejiang ◽  
贾平 Jia Ping ◽  
周达标 Zhou Dabiao ◽  
丁鹏 Ding Peng
Keyword(s):  
Target Detection ◽  
Infrared Image ◽  
Small Target ◽  
Small Target Detection ◽  
Detection Technology

scholarly journals Implementation of a Modified Faster R-CNN for Target Detection Technology of Coastal Defense Radar

2021 ◽  
Vol 13 (9) ◽  
pp. 1703
Author(s):  
He Yan ◽  
Chao Chen ◽  
Guodong Jin ◽  
Jindong Zhang ◽  
Xudong Wang ◽  
...  
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Real Data ◽  
Detection Performance ◽  
Detection Accuracy ◽  
Constant False Alarm Rate ◽  
Data Set ◽  
Detection Technology ◽  
Coastal Defense

The traditional method of constant false-alarm rate detection is based on the assumption of an echo statistical model. The target recognition accuracy rate and the high false-alarm rate under the background of sea clutter and other interferences are very low. Therefore, computer vision technology is widely discussed to improve the detection performance. However, the majority of studies have focused on the synthetic aperture radar because of its high resolution. For the defense radar, the detection performance is not satisfactory because of its low resolution. To this end, we herein propose a novel target detection method for the coastal defense radar based on faster region-based convolutional neural network (Faster R-CNN). The main processing steps are as follows: (1) the Faster R-CNN is selected as the sea-surface target detector because of its high target detection accuracy; (2) a modified Faster R-CNN based on the characteristics of sparsity and small target size in the data set is employed; and (3) soft non-maximum suppression is exploited to eliminate the possible overlapped detection boxes. Furthermore, detailed comparative experiments based on a real data set of coastal defense radar are performed. The mean average precision of the proposed method is improved by 10.86% compared with that of the original Faster R-CNN.

Real-Time Web GIS Analysis Using WebGL

2012 ◽  
Vol 1 (3) ◽  
pp. 49-61 ◽  
Author(s):  
Michael Auer
Keyword(s):  
Parallel Processing ◽  
Real Time ◽  
Large Data ◽  
Web Gis ◽  
3D Graphics ◽  
Gis Analysis ◽  
User Interactions ◽  
Algorithm Performance ◽  
Gis Applications ◽  
Client Side

Parallel processing methods in Geographic Information Systems (GIS) are traditionally used to accelerate the calculation of large data volumes with sophisticated spatial algorithms. Such kinds of acceleration can also be applied to provide real-time GIS applications to improve the responsiveness of user interactions with the data. This paper presents a method to enable this approach for Web GIS applications. It uses the JavaScript 3D graphics API (WebGL) to perform client-side parallel real-time computations of 2D or 2.5D spatial raster algorithms on the graphics card. The potential of this approach is evaluated using an example implementation of a hillshade algorithm. Performance comparisons of parallel and sequential computations reveal acceleration factors between 25 and 100, mainly depending on mobile or desktop environments.

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