1,765 research outputs found
Fast and Robust Small Infrared Target Detection Using Absolute Directional Mean Difference Algorithm
Infrared small target detection in an infrared search and track (IRST) system
is a challenging task. This situation becomes more complicated when high
gray-intensity structural backgrounds appear in the field of view (FoV) of the
infrared seeker. While the majority of the infrared small target detection
algorithms neglect directional information, in this paper, a directional
approach is presented to suppress structural backgrounds and develop a more
effective detection algorithm. To this end, a similar concept to the average
absolute gray difference (AAGD) is utilized to construct a novel directional
small target detection algorithm called absolute directional mean difference
(ADMD). Also, an efficient implementation procedure is presented for the
proposed algorithm. The proposed algorithm effectively enhances the target area
and eliminates background clutter. Simulation results on real infrared images
prove the significant effectiveness of the proposed algorithm.Comment: The Final version (Accepted in Signal Processing journal
RGB-T salient object detection via fusing multi-level CNN features
RGB-induced salient object detection has recently witnessed substantial progress, which is attributed to the superior feature learning capability of deep convolutional neural networks (CNNs). However, such detections suffer from challenging scenarios characterized by cluttered backgrounds, low-light conditions and variations in illumination. Instead of improving RGB based saliency detection, this paper takes advantage of the complementary benefits of RGB and thermal infrared images. Specifically, we propose a novel end-to-end network for multi-modal salient object detection, which turns the challenge of RGB-T saliency detection to a CNN feature fusion problem. To this end, a backbone network (e.g., VGG-16) is first adopted to extract the coarse features from each RGB or thermal infrared image individually, and then several adjacent-depth feature combination (ADFC) modules are designed to extract multi-level refined features for each single-modal input image, considering that features captured at different depths differ in semantic information and visual details. Subsequently, a multi-branch group fusion (MGF) module is employed to capture the cross-modal features by fusing those features from ADFC modules for a RGB-T image pair at each level. Finally, a joint attention guided bi-directional message passing (JABMP) module undertakes the task of saliency prediction via integrating the multi-level fused features from MGF modules. Experimental results on several public RGB-T salient object detection datasets demonstrate the superiorities of our proposed algorithm over the state-of-the-art approaches, especially under challenging conditions, such as poor illumination, complex background and low contrast
Small and Dim Target Detection in IR Imagery: A Review
While there has been significant progress in object detection using
conventional image processing and machine learning algorithms, exploring small
and dim target detection in the IR domain is a relatively new area of study.
The majority of small and dim target detection methods are derived from
conventional object detection algorithms, albeit with some alterations. The
task of detecting small and dim targets in IR imagery is complex. This is
because these targets often need distinct features, the background is cluttered
with unclear details, and the IR signatures of the scene can change over time
due to fluctuations in thermodynamics. The primary objective of this review is
to highlight the progress made in this field. This is the first review in the
field of small and dim target detection in infrared imagery, encompassing
various methodologies ranging from conventional image processing to
cutting-edge deep learning-based approaches. The authors have also introduced a
taxonomy of such approaches. There are two main types of approaches:
methodologies using several frames for detection, and single-frame-based
detection techniques. Single frame-based detection techniques encompass a
diverse range of methods, spanning from traditional image processing-based
approaches to more advanced deep learning methodologies. Our findings indicate
that deep learning approaches perform better than traditional image
processing-based approaches. In addition, a comprehensive compilation of
various available datasets has also been provided. Furthermore, this review
identifies the gaps and limitations in existing techniques, paving the way for
future research and development in this area.Comment: Under Revie
A robust FLIR target detection employing an auto-convergent pulse coupled neural network
© 2019 Informa UK Limited, trading as Taylor & Francis Group. Automatic target detection (ATD) of a small target along with its true shape from highly cluttered forward-looking infrared (FLIR) imagery is crucial. FLIR imagery is low contrast in nature, which makes it difficult to discriminate the target from its immediate background. Here, pulse-coupled neural network (PCNN) is extended with auto-convergent criteria to provide an efficient ATD tool. The proposed auto-convergent PCNN (AC-PCNN) segments the target from its background in an adaptive manner to identify the target region when the target is camouflaged or contains higher visual clutter. Then, selection of region of interest followed by template matching is augmented to capture the accurate shape of a target in a real scenario. The outcomes of the proposed method are validated through well-known statistical methods and found superior performance over other conventional methods
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