scholarly journals High-Speed Recognition Algorithm Based on BRISK and Saliency Detection for Aerial Images

2013 ◽  
Vol 5 (23) ◽  
pp. 5469-5473 ◽  
Author(s):  
Teng-Jiao Xiao ◽  
Dan-Pei Zhao ◽  
Jun Shi ◽  
Ming Lu
Keyword(s):  
High Speed ◽  
Saliency Detection ◽  
Recognition Algorithm ◽  
Aerial Images

Segmentation and Recognition Algorithm for High-Speed Railway Scene

2019 ◽  
Vol 39 (6) ◽  
pp. 0610004
Author(s):  
王洋 Yang Wang ◽  
朱力强 Liqiang Zhu ◽  
余祖俊 Zujun Yu ◽  
郭保青 Baoqing Guo
Keyword(s):  
High Speed ◽  
Recognition Algorithm ◽  
High Speed Railway

IMAGE REGISTRATION AND FUSION SYSTEM BASED ON GPU

2010 ◽  
Vol 19 (01) ◽  
pp. 173-189
Author(s):  
SEUNG-HUN YOO ◽  
CHANG-SUNG JEONG
Keyword(s):  
Visual Information ◽  
High Speed ◽  
Graphics Processing Unit ◽  
Aerial Images ◽  
Processing Unit ◽  
Infrared Sensors ◽  
Fusion Algorithm ◽  
Registration Method ◽  
Multi Scale ◽  
Graphics Processing

Graphics processing unit (GPU) has surfaced as a high-quality platform for computer vision-related systems. In this paper, we propose a straightforward system consisting of a registration and a fusion method over GPU, which generates good results at high speed, compared to non-GPU-based systems. Our GPU-accelerated system utilizes existing methods through converting the methods into the GPU-based platform. The registration method uses point correspondences to find a registering transformation estimated with the incremental parameters in a coarse-to-fine way, while the fusion algorithm uses multi-scale methods to fuse the results from the registration stage. We evaluate performance with the same methods that are executed over both CPU-only and GPU-mounted environment. The experiment results present convincing evidences of the efficiency of our system, which is tested on a few pairs of aerial images taken by electro-optical and infrared sensors to provide visual information of a scene for environmental observatories.

Insulator detection and recognition of explosion based on convolutional neural networks

2019 ◽  
Vol 17 (02) ◽  
pp. 1940008 ◽  
Author(s):  
Bin Yan ◽  
Qing Chen ◽  
Run Ye ◽  
Xiaojia Zhou
Keyword(s):  
Computer Technology ◽  
Transmission Lines ◽  
Saliency Detection ◽  
Recognition Algorithm ◽  
Contour Detection ◽  
High Definition ◽  
Multiple Faults ◽  
Saliency Maps ◽  
Som Network ◽  
Fully Connected

Unmanned aerial vehicles (UAVs) equipped with high definition (HD) cameras can obtain a large number of detailed inspection images. The insulator is an indispensable component in the transmission lines. Detecting insulator in image video quickly and accurately can provide a reliable basis for the ranging and the obstacle avoidance flight of UAV close to the tower and transmission line. At the same time, the insulator is a serious threat to the safety of the power grid due to the multiple faults of the insulator, and the computer technology should be fully utilized to diagnose the fault. Detection of the insulator images with the complex aerial background is implemented by constructing a convolutional neural network (CNN), which has the classic architecture of five modules of convolution and pooling, two modules of fully connected layers. In this paper, we propose a recognition algorithm for explosion fault based on saliency detection, which uses the trained network model to extract the features. Then, we put the saliency maps into a self-organizing feature map (SOM) network and build the mathematical module via super pixel segmentation, contour detection and other image processing methods. The test shows that the algorithm can reduce the error that may be caused by manual analysis. It also demonstrates that the detection of the insulator and the recognition of explosion fault can effectively improve the efficiency and intelligence level.

scholarly journals PRECISION FARMING IN FLORICULTURE

2021 ◽  
Vol 9 (1) ◽  
pp. 75-77
Author(s):  
Manas Mandal ◽  
Bappa Paramanik ◽  
Anamay Sarkar ◽  
Debasis Mahata
Keyword(s):  
High Speed ◽  
Geographical Information Systems ◽  
New Technologies ◽  
Precision Farming ◽  
Modern Technology ◽  
Vital Role ◽  
Production Costs ◽  
Aerial Images ◽  
Geographical Information ◽  
Positioning Systems

Precision farming is a science base modern technology which provided management concept based on observation and response to intra-field variations. New technologies such as Global Positioning Systems (GPS), sensors, satellites or aerial images and Geographical Information Systems (GIS) are utilized to assess and analyse variations in agricultural and horticultural production. In this technology have two primary goals that are (i) optimum return (ii) preserving resource.  Wireless Sensor Networks has crucial role to management of water resources, to assess the optimum point of harvesting, to estimate fertilizer requirements and to predict crop performance more accurately, disease and pest hazard also. Sensors use to precision farming technology in horticulture, which increasing productivity, decreasing production costs and minimizing the environmental impact of farming. Though precision farming has vital role in Agriculture and Horticulture sector but, no so popular due to high cost of technology and need high speed internet facility.

An Anhydrous Bridge Recognition Algorithm in Aerial Image

2013 ◽  
Vol 734-737 ◽  
pp. 3079-3084
Author(s):  
Yin Wen Dong ◽  
Luan Wan ◽  
Zhao Ming Shi ◽  
Jing Xin An
Keyword(s):  
Key Words ◽  
Recognition Algorithm ◽  
Binary Image ◽  
Aerial Images ◽  
Aerial Image ◽  
Geometric Features ◽  
Original Image ◽  
Line Extraction ◽  
Regional Area ◽  
Straight Line

Aiming at anhydrous bridge automatically identification in aerial images, an anhydrous bridge recognition algorithm based on the geometric characteristics is proposed. Firstly, the original image is do threshold segmentation to get binary image. Secondly, binary image is do morphological processed to get bridge area enhanced image and bridge area corrosion image, and these two bridge area are subtracted to extract suspected bridge area based on bridge rectangle feature. Finally, bridge regional area is positioned according to the straight-line characteristics of the bridge. Experimental results show the proposed algorithm can accurately identify the anhydrous bridge effectively. Key words: aerial image; anhydrous bridges identification; edge detection ; straight line extraction ; geometric features

scholarly journals An Adaptive Deblurring Vehicle Detection Method for High-Speed Moving Drones: Resistance to Shake

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1358
Author(s):  
Yan Liu ◽  
Jingwen Wang ◽  
Tiantian Qiu ◽  
Wenting Qi
Keyword(s):  
High Speed ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Input Image ◽  
Research Field ◽  
Aerial Images ◽  
Detection Accuracy ◽  
Intelligent Traffic System ◽  
Blurred Image ◽  
Blurred Images

Vehicle detection is an essential part of an intelligent traffic system, which is an important research field in drone application. Because unmanned aerial vehicles (UAVs) are rarely configured with stable camera platforms, aerial images are easily blurred. There is a challenge for detectors to accurately locate vehicles in blurred images in the target detection process. To improve the detection performance of blurred images, an end-to-end adaptive vehicle detection algorithm (DCNet) for drones is proposed in this article. First, the clarity evaluation module is used to determine adaptively whether the input image is a blurred image using improved information entropy. An improved GAN called Drone-GAN is proposed to enhance the vehicle features of blurred images. Extensive experiments were performed, the results of which show that the proposed method can detect both blurred and clear images well in poor environments (complex illumination and occlusion). The detector proposed achieves larger gains compared with SOTA detectors. The proposed method can enhance the vehicle feature details in blurred images effectively and improve the detection accuracy of blurred aerial images, which shows good performance with regard to resistance to shake.

scholarly journals An Advanced Road Structure Image Segmentation Method using Swt in Cnn

2019 ◽  
Vol 9 (2S3) ◽  
pp. 114-118
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
High Speed ◽  
Learning Phase ◽  
Convolution Neural Network ◽  
Aerial Images ◽  
Aerial Image ◽  
Segmentation Method ◽  
Stroke Width ◽  
Segmentation Approach

In this paper, the design of advanced road structure image segmentation approach using stroke width transformation (SWT) in convolution neural network (CNN) is proposed. The main intent of the proposed system is to acquire the aerial images for the vehicle. Basically, this image segmentation performs its operation in two forms they are operating phase and learning phase. Here the aerial image has enhanced by using the SWT transformation. Hence the main advantage of this proposes system is that it processes the entire operation in simple way with high speed. The SWT will capture the images of road areas in effective way. Hence the propose system has various features which will determine the color, width and many other.

scholarly journals Single-shot face and landmarks detector

2020 ◽  
Vol 44 (4) ◽  
pp. 589-595
Author(s):  
Y.V. Vizilter ◽  
V.S. Gorbatsevich ◽  
A.S. Moiseenko
Keyword(s):  
Face Recognition ◽  
High Speed ◽  
Recognition Algorithm ◽  
Detection Task ◽  
Single Shot ◽  
High Quality ◽  
Facial Landmark Detection ◽  
One Stage ◽  
Biometric Template ◽  
The Face

Facial landmark detection is an important sub-task in solving a number of biometric facial recognition tasks. In face recognition systems, the construction of a biometric template occurs according to a previously aligned (normalized) face image and the normalization stage includes the task of finding facial keypoints. A balance between quality and speed of the facial keypoints detector is important in such a problem. This article proposes a CNN-based one-stage detector of faces and keypoints operating in real time and achieving high quality on a number of well-known test datasets (such as AFLW2000, COFW, Menpo2D). The proposed face and facial landmarks detector is based on the idea of a one-stage SSD object detector, which has established itself as an algorithm that provides high speed and high quality in object detection task. As a basic CNN architecture, we used the ShuffleNet V2 network. An important feature of the proposed algorithm is that the face and facial keypoint detection is done in one CNN forward pass, which can significantly save time at the implementation stage. Also, such multitasking allows one to reduce the percentage of errors in the facial keypoints detection task, which positively affects the final face recognition algorithm quality.

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