UAS Image Detection and Avoidance

2022 ◽  
Author(s):  
Bilkan Ince ◽  
Minguk Seo ◽  
Antonios Tsourdos
Keyword(s):  
Image Detection

Inappropriate thermal image detection system using machine learning

2020 ◽  
Vol 14 (2) ◽  
pp. 102-111
Author(s):  
S. W. Kwon ◽  
I. S. Song ◽  
S. W. Lee ◽  
J. S. Lee ◽  
J. H. Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Thermal Image ◽  
Image Detection

scholarly journals VIPPrint: Validating Synthetic Image Detection and Source Linking Methods on a Large Scale Dataset of Printed Documents

2021 ◽  
Vol 7 (3) ◽  
pp. 50
Author(s):  
Anselmo Ferreira ◽  
Ehsan Nowroozi ◽  
Mauro Barni
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Child Pornography ◽  
Forensic Analysis ◽  
Synthetic Image ◽  
Image Detection ◽  
Face Images ◽  
Large Scale Dataset ◽  
Scanned Images ◽  
Analysis Of The Images

The possibility of carrying out a meaningful forensic analysis on printed and scanned images plays a major role in many applications. First of all, printed documents are often associated with criminal activities, such as terrorist plans, child pornography, and even fake packages. Additionally, printing and scanning can be used to hide the traces of image manipulation or the synthetic nature of images, since the artifacts commonly found in manipulated and synthetic images are gone after the images are printed and scanned. A problem hindering research in this area is the lack of large scale reference datasets to be used for algorithm development and benchmarking. Motivated by this issue, we present a new dataset composed of a large number of synthetic and natural printed face images. To highlight the difficulties associated with the analysis of the images of the dataset, we carried out an extensive set of experiments comparing several printer attribution methods. We also verified that state-of-the-art methods to distinguish natural and synthetic face images fail when applied to print and scanned images. We envision that the availability of the new dataset and the preliminary experiments we carried out will motivate and facilitate further research in this area.

scholarly journals Identification of Skin Lesions by Using Single-Step Multiframe Detector

2021 ◽  
Vol 10 (1) ◽  
pp. 144
Author(s):  
Yu-Ping Hsiao ◽  
Chih-Wei Chiu ◽  
Chih-Wei Lu ◽  
Hong Thai Nguyen ◽  
Yu Sheng Tseng ◽  
...  
Keyword(s):  
Ground Truth ◽  
Skin Lesions ◽  
Single Step ◽  
Single Shot ◽  
Accuracy Rate ◽  
Tissue Slices ◽  
Image Detection ◽  
Sensitivity Rate ◽  
Diagnosis Accuracy ◽  
Artificial Intelligence Algorithm

An artificial intelligence algorithm to detect mycosis fungoides (MF), psoriasis (PSO), and atopic dermatitis (AD) is demonstrated. Results showed that 10 s was consumed by the single shot multibox detector (SSD) model to analyze 292 test images, among which 273 images were correctly detected. Verification of ground truth samples of this research come from pathological tissue slices and OCT analysis. The SSD diagnosis accuracy rate was 93%. The sensitivity values of the SSD model in diagnosing the skin lesions according to the symptoms of PSO, AD, MF, and normal were 96%, 80%, 94%, and 95%, and the corresponding precision were 96%, 86%, 98%, and 90%. The highest sensitivity rate was found in MF probably because of the spread of cancer cells in the skin and relatively large lesions of MF. Many differences were found in the accuracy between AD and the other diseases. The collected AD images were all in the elbow or arm and other joints, the area with AD was small, and the features were not obvious. Hence, the proposed SSD could be used to identify the four diseases by using skin image detection, but the diagnosis of AD was relatively poor.

scholarly journals Automatic Detection Technology of Sonar Image Target Based on the Three-Dimensional Imaging

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Wanzeng Kong ◽  
Jinshuai Yu ◽  
Ying Cheng ◽  
Weihua Cong ◽  
Huanhuan Xue
Keyword(s):  
Data Storage ◽  
3D Imaging ◽  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Automatic Detection ◽  
Classification Model ◽  
Image Detection ◽  
Practical Applications ◽  
Sonar Image ◽  
Detection Technology

With 3D imaging of the multisonar beam and serious interference of image noise, detecting objects based only on manual operation is inefficient and also not conducive to data storage and maintenance. In this paper, a set of sonar image automatic detection technologies based on 3D imaging is developed to satisfy the actual requirements in sonar image detection. Firstly, preprocessing was conducted to alleviate the noise and then the approximate position of object was obtained by calculating the signal-to-noise ratio of each target. Secondly, the separation of water bodies and strata is realized by maximum variance between clusters (OTSU) since there exist obvious differences between these two areas. Thus image segmentation can be easily implemented on both. Finally, the feature extraction is carried out, and the multidimensional Bayesian classification model is established to do classification. Experimental results show that the sonar-image-detection technology can effectively detect the target and meet the requirements of practical applications.

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