Miss Detection vs. False Alarm: Adversarial Learning for Small Object Segmentation in Infrared Images

2019 ◽  
Author(s):  
Huan Wang ◽  
Luping Zhou ◽  
Lei Wang
Keyword(s):  
False Alarm ◽  
Object Segmentation ◽  
Small Object ◽  
Infrared Images ◽  
Adversarial Learning

AVILNet: A New Pliable Network with a Novel Metric for Small-Object Segmentation and Detection in Infrared Images

2021 ◽  
Vol 13 (4) ◽  
pp. 555
Author(s):  
Ikhwan Song ◽  
Sungho Kim
Keyword(s):  
Object Segmentation ◽  
State Of The Art ◽  
Threshold Value ◽  
Small Object ◽  
Infrared Images ◽  
Average Precision ◽  
Trade Off ◽  
Good Evaluation ◽  
Single Dataset ◽  
Evaluation Metric

Infrared small-object segmentation (ISOS) has a persistent trade-off problem—that is, which came first, recall or precision? Constructing a fine balance between of them is, au fond, of vital importance to obtain the best performance in real applications, such as surveillance, tracking, and many fields related to infrared searching and tracking. F1-score may be a good evaluation metric for this problem. However, since the F1-score only depends upon a specific threshold value, it cannot reflect the user’s requirements according to the various application environment. Therefore, several metrics are commonly used together. Now we introduce F-area, a novel metric for a panoptic evaluation of average precision and F1-score. It can simultaneously consider the performance in terms of real application and the potential capability of a model. Furthermore, we propose a new network, called the Amorphous Variable Inter-located Network (AVILNet), which is of pliable structure based on GridNet, and it is also an ensemble network consisting of the main and its sub-network. Compared with the state-of-the-art ISOS methods, our model achieved an AP of 51.69%, F1-score of 63.03%, and F-area of 32.58% on the International Conference on Computer Vision 2019 ISOS Single dataset by using one generator. In addition, an AP of 53.6%, an F1-score of 60.99%, and F-area of 32.69% by using dual generators, with beating the existing best record (AP, 51.42%; F1-score, 57.04%; and F-area, 29.33%).

scholarly journals Concealed object segmentation in terahertz imaging via adversarial learning

Optik ◽  
2019 ◽  
Vol 185 ◽  
pp. 1104-1114 ◽  
Author(s):  
Dong Liang ◽  
Jiaxing Pan ◽  
Yang Yu ◽  
Huiyu Zhou
Keyword(s):  
Object Segmentation ◽  
Terahertz Imaging ◽  
Adversarial Learning

scholarly journals Fire detection with infrared images using cascaded neural network

2019 ◽  
Vol 13 ◽  
pp. 174830261989543
Author(s):  
Li Deng ◽  
Qian Chen ◽  
Yuanhua He ◽  
Xiubao Sui ◽  
Quanyi Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
False Alarm ◽  
False Alarm Rate ◽  
Early Stage ◽  
Infrared Camera ◽  
Fire Detection ◽  
Image Texture ◽  
Infrared Images ◽  
Detection Technology ◽  
Civil Aircraft

The existing equipment of civil aircraft cargo fire detection mainly uses photoelectric smoke detectors, which has a high false alarm rate. According to Federal Aviation Agency’s (FAA) statistics, the false alarm rate is as high as 99%. 1 In the cargo of civil aircraft, the traditional photoelectric detection technology cannot effectively distinguish interference particles from smoke particles. Since the video smoke detection technology has proven to be reliable in many large scenarios, a deep learning method of image processing for fire detection is proposed. The proposed convolutional neural network is constructed of front end network and back end network cascaded with the capsule network and the circularity computation for the dynamic infrared fire image texture extraction. In order to accurately identify whether there is a fire in the area and give the kind of burning substances, a series of fuels are selected, such as n-heptane, cyclohexane, and carton for combustion reaction, and infrared camera is used to take infrared images of all fuel combustion. Experimental results show that the proposed method can effectively detect fire at the early stage of fire which is applicable for fire detection in civil aircraft cargoes.

Small object segmentation with fully convolutional network based on overlapping domain decomposition

2019 ◽  
Vol 30 (4) ◽  
pp. 707-716 ◽  
Author(s):  
Jinhee Park ◽  
Dokyeong Kwon ◽  
Bo Won Choi ◽  
Ga Young Kim ◽  
Kwang Yong Kim ◽  
...  
Keyword(s):  
Domain Decomposition ◽  
Object Segmentation ◽  
Small Object ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Overlapping Domain Decomposition

scholarly journals Lesion-wise evaluation for effective performance monitoring of small object segmentation

2021 ◽  
Author(s):  
Irme Groothuis ◽  
Carole Sudre ◽  
Silvia Ingala ◽  
Josephine Barnes ◽  
Juan Domingo Gispert Lopez ◽  
...  
Keyword(s):  
Performance Monitoring ◽  
Object Segmentation ◽  
Small Object ◽  
Effective Performance

scholarly journals Image based analysis of meibomian gland dysfunction using conditional generative adversarial neural network

2021 ◽  
Vol 6 (1) ◽  
pp. e000436
Author(s):  
Zakir Khan Khan ◽  
Arif Iqbal Umar ◽  
Syed Hamad Shirazi ◽  
Asad Rasheed ◽  
Abdul Qadir ◽  
...  
Keyword(s):  
Pearson Correlation ◽  
Meibomian Gland ◽  
Meibomian Gland Dysfunction ◽  
Infrared Images ◽  
Generative Adversarial Network ◽  
Adversarial Learning ◽  
Binary Images ◽  
Adversarial Network ◽  
Disease Analysis

ObjectiveMeibomian gland dysfunction (MGD) is a primary cause of dry eye disease. Analysis of MGD, its severity, shapes and variation in the acini of the meibomian glands (MGs) is receiving much attention in ophthalmology clinics. Existing methods for diagnosing, detection and analysing meibomianitis are not capable to quantify the irregularities to IR (infrared) images of MG area such as light reflection, interglands and intraglands boundaries, the improper focus of the light and positioning, and eyelid eversion.Methods and analysisWe proposed a model that is based on adversarial learning that is, conditional generative adversarial network that can overcome these blatant challenges. The generator of the model learns the mapping from the IR images of the MG to a confidence map specifying the probabilities of being a pixel of MG. The discriminative part of the model is responsible to penalise the mismatch between the IR images of the MG and confidence map. Furthermore, the adversarial learning assists the generator to produce a qualitative confidence map which is transformed into binary images with the help of fixed thresholding to fulfil the segmentation of MG. We identified MGs and interglands boundaries from IR images.ResultsThis method is evaluated by meiboscoring, grading, Pearson correlation and Bland-Altman analysis. We also judged the quality of our method through average Pompeiu-Hausdorff distance, and Aggregated Jaccard Index.ConclusionsThis technique provides a significant improvement in the quantification of the irregularities to IR. This technique has outperformed the state-of-art results for the detection and analysis of the dropout area of MGD.

Sign in / Sign up

Export Citation Format

Share Document