scholarly journals Computerized or Automated Object Recognition and Analysis of Pattern Matching in Runways Using Surface Track Data

2021 ◽  
Vol 23 (11) ◽  
pp. 159-165
Author(s):  
JAYANTH DWIJESH H P ◽  
◽  
SANDEEP S V ◽  
RASHMI S ◽  
◽  
...  
Keyword(s):  
Edge Detection ◽  
Object Detection ◽  
Pattern Matching ◽  
Aerial Images ◽  
3 Dimensional ◽  
Detection Techniques ◽  
Detection Algorithms ◽  
Detection Analysis ◽  
Efficient Detection ◽  
Minimal Damage

In today’s world, accurate and fast information is vital for safe aircraft landings. The purpose of an EMAS (Engineered Materials Arresting System) is to prevent an aeroplane from overrunning with no human injury and minimal damage to the aircraft. Although various algorithms for object detection analysis have been developed, only a few researchers have examined image analysis as a landing assist. Image intensity edges are employed in one system to detect the sides of a runway in an image sequence, allowing the runway’s 3-dimensional position and orientation to be approximated. A fuzzy network system is used to improve object detection and extraction from aerial images. In another system, multi-scale, multiplatform imagery is used to combine physiologically and geometrically inspired algorithms for recognizing objects from hyper spectral and/or multispectral (HS/MS) imagery. However, the similarity in the top view of runways, buildings, highways, and other objects is a disadvantage of these methods. We propose a new method for detecting and tracking the runway based on pattern matching and texture analysis of digital images captured by aircraft cameras. Edge detection techniques are used to recognize runways from aerial images. The edge detection algorithms employed in this paper are the Hough Transform, Canny Filter, and Sobel Filter algorithms, which result in efficient detection.

scholarly journals Disruptive coloration, crypsis and edge detection in early visual processing

2006 ◽  
Vol 273 (1598) ◽  
pp. 2141-2147 ◽  
Author(s):  
Martin Stevens ◽  
Innes C Cuthill
Keyword(s):  
Edge Detection ◽  
Pattern Matching ◽  
Visual Processing ◽  
Colour Vision ◽  
The Body ◽  
Detection Algorithms ◽  
Avian Predators ◽  
Early Visual Processing ◽  
Disruptive Coloration ◽  
Risk Of Predation

Many animals use concealing markings to reduce the risk of predation. These include background pattern matching (crypsis), where the coloration matches a random sample of the background and disruptive patterns, whose effectiveness has been hypothesized to lie in breaking up the body into a series of apparently unrelated objects. We have previously established the effectiveness of disruptive coloration against avian predators, using artificial moth-like stimuli with colours designed to match natural backgrounds as perceived by birds. Here, we investigate the mechanism by which disruptive patterns reduce detectability, using a computational vision model of edge detection applied to photographs of our experimental stimuli, calibrated for bird colour vision. We show that, disruptive coloration is effective by exploiting edge detection algorithms that we use to model early visual processing. Thus, ‘false’ edges are detected within the body rather than at its periphery, so inhibiting successful detection of the animal's body outline.

scholarly journals GLE-Net: A Global and Local Ensemble Network for Aerial Object Detection

2022 ◽  
Vol 15 (1) ◽  
Author(s):  
Jiajia Liao ◽  
Yujun Liu ◽  
Yingchao Piao ◽  
Jinhe Su ◽  
Guorong Cai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Aerial Images ◽  
Visual Object ◽  
Detection Algorithms ◽  
Integration Strategy ◽  
Bounding Boxes ◽  
Global And Local ◽  
Deep Learning Model

AbstractRecent advances in camera-equipped drone applications increased the demand for visual object detection algorithms with deep learning for aerial images. There are several limitations in accuracy for a single deep learning model. Inspired by ensemble learning can significantly improve the generalization ability of the model in the machine learning field, we introduce a novel integration strategy to combine the inference results of two different methods without non-maximum suppression. In this paper, a global and local ensemble network (GLE-Net) was proposed to increase the quality of predictions by considering the global weights for different models and adjusting the local weights for bounding boxes. Specifically, the global module assigns different weights to models. In the local module, we group the bounding boxes that corresponding to the same object as a cluster. Each cluster generates a final predict box and assigns the highest score in the cluster as the score of the final predict box. Experiments on benchmarks VisDrone2019 show promising performance of GLE-Net compared with the baseline network.

scholarly journals A Low Redundancy Wavelet Entropy Edge Detection Algorithm

2021 ◽  
Vol 7 (9) ◽  
pp. 188
Author(s):  
Yiting Tao ◽  
Thomas Scully ◽  
Asanka G. Perera ◽  
Andrew Lambert ◽  
Javaan Chahl
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Shannon Entropy ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
Detection Techniques ◽  
Detection Algorithms ◽  
Real Time Image ◽  
Time Image ◽  
Vision Application

Fast edge detection of images can be useful for many real-world applications. Edge detection is not an end application but often the first step of a computer vision application. Therefore, fast and simple edge detection techniques are important for efficient image processing. In this work, we propose a new edge detection algorithm using a combination of the wavelet transform, Shannon Entropy and thresholding. The new algorithm is based on the concept that each Wavelet decomposition level has an assumed level of structure that enables the use of Shannon entropy as a measure of global image structure. The proposed algorithm is developed mathematically and compared to five popular edge detection algorithms. The results show that our solution is low redundancy, noise resilient, and well suited to real-time image processing applications.

scholarly journals MAGNet: A Camouflage Object Detection Network Simulating The Observation Effect of Magnifier

2021 ◽  
Author(s):  
Xinhao Jiang ◽  
Wei Cai ◽  
Bo Jiang ◽  
Zhiyong Yang ◽  
Xin Wang
Keyword(s):  
Edge Detection ◽  
Object Detection ◽  
Target Detection ◽  
The Public ◽  
Detection Techniques ◽  
Human Attention ◽  
Attention Focus ◽  
Observation Process ◽  
Segmentation Accuracy ◽  
High Degree

Abstract In recent years, protecting important objects by simulating animal camouflage has been widely used in many fields. Therefore, the Camouflaged Object Detection (COD) technology has emerged. COD is more difficult than traditional target detection techniques because of the high degree of fusion of camouflaged objects with the background. In this paper, we strive to identify camouflaged objects more accurately. Inspired by humans using a magnifier to search for hidden objects in pictures, we propose a COD network that simulates the observation effect of a magnifier, termed Magnifier Network (MAGNet). Specifically, our MAGNet contains two parallel modules, i.e., Ergodic Magnify module (EMM) and Attention Focus module (AFM). The EMM is designed to mimic the magnifying process of a magnifier ergodicing an image, and the AFM is used to perform the observation process in which human attention is highly focused for focusing on a region. The two sets output camouflaged object maps are merged to achieve the effect of simulating the observation of the object by a magnifier. Extensive experiments demonstrate that compared with 14 cutting-edge detection models, the MAGNet can achieve the best comprehensive effect of 8 evaluation indicators on the public COD dataset, and the segmentation accuracy is significantly improved.

scholarly journals MRFF-YOLO: A Multi-Receptive Fields Fusion Network for Remote Sensing Target Detection

2020 ◽  
Vol 12 (19) ◽  
pp. 3118
Author(s):  
Danqing Xu ◽  
Yiquan Wu
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Target Detection ◽  
State Of The Art ◽  
Receptive Fields ◽  
Aerial Images ◽  
Detection Rates ◽  
Detection Algorithms ◽  
Proposed Model ◽  
Small Targets

High-altitude remote sensing target detection has problems related to its low precision and low detection rate. In order to enhance the performance of detecting remote sensing targets, a new YOLO (You Only Look Once)-V3-based algorithm was proposed. In our improved YOLO-V3, we introduced the concept of multi-receptive fields to enhance the performance of feature extraction. Therefore, the proposed model was termed Multi-Receptive Fields Fusion YOLO (MRFF-YOLO). In addition, to address the flaws of YOLO-V3 in detecting small targets, we increased the detection layers from three to four. Moreover, in order to avoid gradient fading, the structure of improved DenseNet was chosen in the detection layers. We compared our approach (MRFF-YOLO) with YOLO-V3 and other state-of-the-art target detection algorithms on an Remote Sensing Object Detection (RSOD) dataset and a dataset of Object Detection in Aerial Images (UCS-AOD). With a series of improvements, the mAP (mean average precision) of MRFF-YOLO increased from 77.10% to 88.33% in the RSOD dataset and increased from 75.67% to 90.76% in the UCS-AOD dataset. The leaking detection rates are also greatly reduced, especially for small targets. The experimental results showed that our approach achieved better performance than traditional YOLO-V3 and other state-of-the-art models for remote sensing target detection.

An Edge Exposure using Caliber Fuzzy C-means With Canny Algorithm

2017 ◽  
Vol 8 (1) ◽  
pp. 59
Author(s):  
Gowri Jeyaraman ◽  
Janakiraman Subbiah
Keyword(s):  
Edge Detection ◽  
Clustering Algorithm ◽  
Detection Algorithm ◽  
Cluster Center ◽  
Detection Techniques ◽  
Fuzzy C Means ◽  
Classical Study ◽  
Detection Algorithms ◽  
Initial Cluster ◽  
Canny Algorithm

<p>Edge exposure or edge detection is an important and classical study of the medical field and computer vision.  Caliber Fuzzy C-means (CFCM) clustering Algorithm for edge detection depends on the selection of initial cluster center value. This endeavor to put in order a collection of pixels into a cluster, such that a pixel within the cluster must be more comparable to every other pixel. Using CFCM techniques first cluster the BSDS image, next the clustered image is given as an input to the basic canny edge detection algorithm. The application of new parameters with fewer operations for CFCM is fruitful. According to the calculation, a result acquired by using CFCM clustering function divides the image into four clusters in common. The proposed method is evidently robust into the modification of fuzzy c-means and canny algorithm. The convergence of this algorithm is very speedy compare to the entire edge detection algorithms. The consequences of this proposed algorithm make enhanced edge detection and better result than any other traditional image edge detection techniques.</p>

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