Experimental Results of Air Target Detection With a GPS Forward-Scattering Radar

2012 ◽  
Vol 9 (1) ◽  
pp. 47-51 ◽  
Author(s):  
Ion Suberviola ◽  
Iker Mayordomo ◽  
Jaizki Mendizabal
Keyword(s):  
Target Detection ◽  
Forward Scattering ◽  
Experimental Results

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.

scholarly journals Research on driverless vehicle vision algorithm

2021 ◽  
Vol 336 ◽  
pp. 06001
Author(s):  
Xinchao Liu ◽  
Ying Yan ◽  
Haiyun Gan
Keyword(s):  
Target Detection ◽  
Learning Algorithm ◽  
Detection Algorithm ◽  
Obstacle Detection ◽  
Urban Traffic ◽  
Experimental Results ◽  
Deep Learning Algorithm ◽  
Deep Model ◽  
Traffic Environment ◽  
Vision Algorithm

Obstacle detection in complex urban traffic environment has become an important part of unmanned vehicle optimization, and its complexity brings great challenges to the reliability of unmanned target detection. YOLOv3 in deep learning algorithm has a good detection effect in target detection, but it has certain defects in detecting targets in complex urban traffic environment. In this paper, the spatial pyramid module is added to YOLOv3 to improve the extraction of data features of the deep model. Then, on the basis of optimized network, the target detection algorithm is streamlined by combining layer pruning and channel pruning. The streamlined algorithm is called YOLOv3-SPP3-Tiny. Comparing the experimental results of YOLOv3-SPP3-tiny and YOLOv3 on Street Scenes dataset, the Precision is improved by 2.77%, the average precision (mAP) is increased by 0.87%, the Total BFLOPS is reduced by 94.49%, and the Inference time is reduced by 80.39%. Experimental results show that the model YOLOv3-SPP3-tiny algorithm is more conducive to unmanned object detection in complex urban road environment.

scholarly journals Practical Approaches to Target Detection in Long Range and Low Quality Infrared Videos

2021 ◽  
Vol 12 (3) ◽  
pp. 01-16
Author(s):  
Chiman Kwan ◽  
David Gribben
Keyword(s):  
Deep Learning ◽  
Long Range ◽  
Target Detection ◽  
Experimental Results ◽  
Target Size ◽  
Small Target ◽  
Performance Improvements ◽  
Texture Information ◽  
Learning Techniques ◽  
Small Targets

It is challenging to detect vehicles in long range and low quality infrared videos using deep learning techniques such as You Only Look Once (YOLO) mainly due to small target size. This is because small targets do not have detailed texture information. This paper focuses on practical approaches for target detection in infrared videos using deep learning techniques. We first investigated a newer version of You Only Look Once (YOLO v4). We then proposed a practical and effective approach by training the YOLO model using videos from longer ranges. Experimental results using real infrared videos ranging from 1000 m to 3500 m demonstrated huge performance improvements. In particular, the average detection percentage over the six ranges of 1000 m to 3500 m improved from 54% when we used the 1500 m videos for training to 95% if we used the 3000 m videos for training.

scholarly journals Background Information Self-Learning Based Hyperspectral Target Detection

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Yufei Tian ◽  
Jihai Yang ◽  
Shijun Li ◽  
Wenning Xu
Keyword(s):  
Information Extraction ◽  
Hyperspectral Imaging ◽  
Target Detection ◽  
Detection Method ◽  
Experimental Results ◽  
Hyperspectral Images ◽  
Background Information ◽  
Spectral Information ◽  
Tumor Region ◽  
Self Learning

Hyperspectral imaging has been proved as an effective way to explore the useful information behind the land objects. And it can also be adopted for biologic information extraction, by which the origin information can be acquired from the image repeatedly without contamination. In this paper we proposed a target detection method based on background self-learning to extract the biologic information from the hyperspectral images. The conventional unstructured target detectors are very difficult to estimate the background statistics accurately in either a global or local way. Considering the spatial spectral information, its performance can be further improved by avoiding the above problem. It is especially designed to extract fingerprint and tumor region from hyperspectral biologic images. The experimental results show the validity and the superiority of our method on detecting the biologic information from hyperspectral images.

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