scholarly journals FPGA-Based Real-Time Moving Target Detection System for Unmanned Aerial Vehicle Application

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Jia Wei Tang ◽  
Nasir Shaikh-Husin ◽  
Usman Ullah Sheikh ◽  
M. N. Marsono
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Target Detection ◽  
Detection System ◽  
Detection Algorithm ◽  
Moving Target ◽  
Moving Target Detection ◽  
Rescue Operation ◽  
Aerial Vehicle

Moving target detection is the most common task for Unmanned Aerial Vehicle (UAV) to find and track object of interest from a bird’s eye view in mobile aerial surveillance for civilian applications such as search and rescue operation. The complex detection algorithm can be implemented in a real-time embedded system using Field Programmable Gate Array (FPGA). This paper presents the development of real-time moving target detection System-on-Chip (SoC) using FPGA for deployment on a UAV. The detection algorithm utilizes area-based image registration technique which includes motion estimation and object segmentation processes. The moving target detection system has been prototyped on a low-cost Terasic DE2-115 board mounted with TRDB-D5M camera. The system consists of Nios II processor and stream-oriented dedicated hardware accelerators running at 100 MHz clock rate, achieving 30-frame per second processing speed for 640 × 480 pixels’ resolution greyscale videos.

scholarly journals Detection and Adaptive Video Processing of Hyperopia Scene in Sports Video

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Qingjie Chen ◽  
Minkai Dong
Keyword(s):  
Real Time ◽  
Target Detection ◽  
Motion Detection ◽  
Difference Method ◽  
Detection Algorithm ◽  
Moving Target ◽  
Target Area ◽  
Moving Target Detection ◽  
Target Extraction ◽  
Motion Video

In the research of motion video, the existing target detection methods are susceptible to changes in the motion video scene and cannot accurately detect the motion state of the target. Moving target detection technology is an important branch of computer vision technology. Its function is to implement real-time monitoring, real-time video capture, and detection of objects in the target area and store information that users are interested in as an important basis for exercise. This article focuses on how to efficiently perform motion detection on real-time video. By introducing the mathematical model of image processing, the traditional motion detection algorithm is improved and the improved motion detection algorithm is implemented in the system. This article combines the advantages of the widely used frame difference method, target detection algorithm, and background difference method and introduces the moving object detection method combining these two algorithms. When using Gaussian mixture model for modeling, improve the parts with differences, and keep the unmatched Gaussian distribution so that the modeling effect is similar to the actual background; the binary image is obtained through the difference between frames and the threshold, and the motion change domain is extracted through mathematical morphological filtering, and finally, the moving target is detected. The experiment proved the following: when there are more motion states, the recall rate is slightly better than that of the VIBE algorithm. It decreased about 0.05 or so, but the relative accuracy rate increased by about 0.12, and the increase ratio is significantly higher than the decrease ratio. Departments need to adopt effective target extraction methods. In order to improve the accuracy of moving target detection, this paper studies the method of background model establishment and target extraction and proposes its own improvement.

scholarly journals Parallel Ensemble Deep Learning for Real-Time Remote Sensing Video Multi-Target Detection

2021 ◽  
Vol 13 (21) ◽  
pp. 4377
Author(s):  
Long Sun ◽  
Jie Chen ◽  
Dazheng Feng ◽  
Mengdao Xing
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Target Detection ◽  
Template Matching ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Information Warfare ◽  
Aerial Vehicle ◽  
Uav Images

Unmanned aerial vehicle (UAV) is one of the main means of information warfare, such as in battlefield cruises, reconnaissance, and military strikes. Rapid detection and accurate recognition of key targets in UAV images are the basis of subsequent military tasks. The UAV image has characteristics of high resolution and small target size, and in practical application, the detection speed is often required to be fast. Existing algorithms are not able to achieve an effective trade-off between detection accuracy and speed. Therefore, this paper proposes a parallel ensemble deep learning framework for unmanned aerial vehicle video multi-target detection, which is a global and local joint detection strategy. It combines a deep learning target detection algorithm with template matching to make full use of image information. It also integrates multi-process and multi-threading mechanisms to speed up processing. Experiments show that the system has high detection accuracy for targets with focal lengths varying from one to ten times. At the same time, the real-time and stable display of detection results is realized by aiming at the moving UAV video image.

Real-Time Moving Target Detection System Based on FPGA

2021 ◽  
Author(s):  
Hankai Yang ◽  
Huayu Wang ◽  
Wei Wei ◽  
Lin Guo ◽  
Kai Sun
Keyword(s):  
Real Time ◽  
Target Detection ◽  
Detection System ◽  
Moving Target ◽  
Moving Target Detection

scholarly journals Design of Citrus Fruit Detection System Based on Mobile Platform and Edge Computer Device

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 59
Author(s):  
Heqing Huang ◽  
Tongbin Huang ◽  
Zhen Li ◽  
Shilei Lyu ◽  
Tao Hong
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Target Detection ◽  
Detection System ◽  
Citrus Fruit ◽  
Detection Algorithm ◽  
Edge Computing ◽  
Detection Accuracy ◽  
Computing Device ◽  
Adaptive Fusion ◽  
Aerial Vehicle

Citrus fruit detection can provide technical support for fine management and yield determination of citrus orchards. Accurate detection of citrus fruits in mountain orchards is challenging because of leaf occlusion and citrus fruit mutual occlusion of different fruits. This paper presents a citrus detection task that combines UAV data collection, AI embedded device, and target detection algorithm. The system used a small unmanned aerial vehicle equipped with a camera to take full-scale pictures of citrus trees; at the same time, we extended the state-of-the-art model target detection algorithm, added the attention mechanism and adaptive fusion feature method, improved the model’s performance; to facilitate the deployment of the model, we used the pruning method to reduce the amount of model calculation and parameters. The improved target detection algorithm is ported to the edge computing end to detect the data collected by the unmanned aerial vehicle. The experiment was performed on the self-made citrus dataset, the detection accuracy was 93.32%, and the processing speed at the edge computing device was 180 ms/frame. This method is suitable for citrus detection tasks in the mountainous orchard environment, and it can help fruit growers to estimate their yield.

Moving Target Detection Based on Principle of Connectivity and Texture Gradient

2012 ◽  
Vol 605-607 ◽  
pp. 2117-2120
Author(s):  
Min Huang ◽  
Yang Zhang ◽  
Gang Chen ◽  
Guo Feng Yang
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Texture Gradient ◽  
Moving Target ◽  
Moving Target Detection ◽  
Gradient Information ◽  
Target Segmentation ◽  
Target Profile

In target detection, “hole” phenomenon is present in the detection result, and the shadow is difficult to remove. To solve these problems, we propose a target detection algorithm based on principle of connectivity and texture gradient. Firstly, we use the connectivity principle to find the largest target prospects connection area to get a complete target contour, secondly we use target texture gradient information to further remove the shadow of the target. At last, the experimental results show that the algorithm can obtain a clear target profile and improve the accuracy of the moving target segmentation.

Dim moving target detection algorithm based on spatio-temporal classification sparse representation

2014 ◽  
Vol 67 ◽  
pp. 273-282 ◽  
Author(s):  
Zhengzhou Li ◽  
Zhen Dai ◽  
Hongxia Fu ◽  
Qian Hou ◽  
Zhen Wang ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Target Detection ◽  
Detection Algorithm ◽  
Moving Target ◽  
Moving Target Detection ◽  
Spatio Temporal
Sign in / Sign up

Export Citation Format

Share Document