Target Detection Task Research Articles

Spontaneous blinking and brain health: Can blink‐related oscillations capture brain changes in aging?

AbstractBackgroundBlink‐related oscillations (BROs) are recently discovered neurophysiological responses that follow spontaneous blinking, and represent environmental monitoring and awareness processes as the brain evaluates new visual information after eye reopening. BROs strongly activate the precuneus which is one of the earliest brain regions impacted by Alzheimer’s disease (AD) neuropathology, and as such may provide an important avenue for assessing and monitoring brain health in aging. In this study, we conducted the first investigation of BRO effects in normal aging to determine whether these responses can capture aging‐related brain changes.MethodThis research used data from the Cambridge Centre for Aging and Neuroscience (Cam‐CAN) repository, which comprises magnetocenphalography (MEG) and structural magnetic resonance imaging (MRI) from 700 healthy adults aged 18‐88. The MEG paradigm consists of a target detection task with simultaneous auditory and visual stimulation. MEG data were first denoised using independent component analysis, then BRO responses were derived by segmenting the cleaned data into 3s epochs time‐locked to spontaneous blinking. Participants were divided into 4 age groups comprising the youngest (18‐30), middle‐young (31‐50), middle‐old (51‐70), and oldest (71‐90), and analyses were performed using global field power (GFP) and minimum norm estimates to examine BRO effects at the sensor‐ and source‐levels, respectively.ResultResults showed that BRO responses were present in all age groups. GFP amplitude increased with age during intervals spanning post‐blink BRO peaks, but not the pre‐blink baseline (p<0.05). BROs also activated brain regions spanning the occipital, temporal, and parietal cortices in all age groups (p<0.05 FWE), with the extent of activation increasing with age. Moreover, the activation extent of precuneus and hippocampus regions showed inverted U‐shape with age, increasing towards the middle‐old group but decreasing in the oldest.ConclusionThese results demonstrate for the first time that BRO responses can capture brain changes in normal aging, with increased neural activation for processing blink‐related information as one ages. Results suggest that BROs may represent a simple yet powerful avenue for measuring aging‐related brain function changes, providing a potential tool for early detection of neurodegeneration – particularly with respect to the precuneus and hippocampus regions highly implicated in AD.

The Effects of Working Memory Load on Auditory Distraction in Adults With Attention Deficit Hyperactivity Disorder.

Cognitive control provides us with the ability to inter alia, regulate the locus of attention and ignore environmental distractions in accordance with our goals. Auditory distraction is a frequently cited symptom in adults with attention deficit hyperactivity disorder (aADHD)–yet few task-based fMRI studies have explored whether deficits in cognitive control (associated with the disorder) impedes on the ability to suppress/compensate for exogenously evoked cortical responses to noise in this population. In the current study, we explored the effects of auditory distraction as function of working memory (WM) load. Participants completed two tasks: an auditory target detection (ATD) task in which the goal was to actively detect salient oddball tones amidst a stream of standard tones in noise, and a visual n-back task consisting of 0-, 1-, and 2-back WM conditions whilst concurrently ignoring the same tonal signal from the ATD task. Results indicated that our sample of young aADHD (n = 17), compared to typically developed controls (n = 17), had difficulty attenuating auditory cortical responses to the task-irrelevant sound when WM demands were high (2-back). Heightened auditory activity to task-irrelevant sound was associated with both poorer WM performance and symptomatic inattentiveness. In the ATD task, we observed a significant increase in functional communications between auditory and salience networks in aADHD. Because performance outcomes were on par with controls for this task, we suggest that this increased functional connectivity in aADHD was likely an adaptive mechanism for suboptimal listening conditions. Taken together, our results indicate that aADHD are more susceptible to noise interference when they are engaged in a primary task. The ability to cope with auditory distraction appears to be related to the WM demands of the task and thus the capacity to deploy cognitive control.

Hyperspectral Target Detection with an Auxiliary Generative Adversarial Network

In recent years, the deep neural network has shown a strong presence in classification tasks and its effectiveness has been well proved. However, the framework of DNN usually requires a large number of samples. Compared to the training sets in classification tasks, the training sets for the target detection of hyperspectral images may only include a few target spectra which are quite limited and precious. The insufficient labeled samples make the DNN-based hyperspectral target detection task a challenging problem. To address this problem, we propose a hyperspectral target detection approach with an auxiliary generative adversarial network. Specifically, the training set is first expanded by generating simulated target spectra and background spectra using the generative adversarial network. Then, a classifier which is highly associated with the discriminator of the generative adversarial network is trained based on the real and the generated spectra. Finally, in order to further suppress the background, guided filters are utilized to improve the smoothness and robustness of the detection results. Experiments conducted on real hyperspectral images show the proposed approach is able to perform more efficiently and accurately compared to other target detection approaches.

Effects of changes in end-tidal PO2 and PCO2 on neural responses during rest and sustained attention.

Impairments of cognitive function during alterations in arterial blood gases (e.g., high‐altitude hypoxia) may result from the disruption of neurovascular coupling; however, the link between changes in arterial blood gases, cognition, and cerebral blood flow (CBF) is poorly understood. To interrogate this link, we developed a multimodal empirical strategy capable of monitoring neural correlates of cognition and CBF simultaneously. Human participants performed a sustained attention task during hypoxia, hypercapnia, hypocapnia, and normoxia while electroencephalographic (EEG) activity and CBF (middle and posterior cerebral arteries; transcranial Doppler ultrasound) were simultaneously measured. The protocol alternated between rest and engaging in a visual target detection task that required participants to monitor a sequence of brief‐duration colored circles and detect infrequent, longer duration circles (targets). The target detection task was overlaid on a large, circular checkerboard that provided robust visual stimulation. Spectral decomposition and event‐related potential (ERP) analyses were applied to the EEG data to investigate spontaneous and task‐specific fluctuations in neural activity. There were three main sets of findings: (1) spontaneous alpha oscillatory activity was modulated as a function of arterial CO2 (hypocapnia and hypercapnia), (2) task‐related neurovascular coupling was disrupted by all arterial blood gas manipulations, and (3) changes in task‐related alpha and theta band activity and attenuation of the P3 ERP component amplitude were observed during hypocapnia. Since alpha and theta are linked with suppression of visual processing and executive control and P3 amplitude with task difficulty, these data suggest that transient arterial blood gas changes can modulate multiple stages of cognitive information processing.

Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image.

This exploration primarily aims to jointly apply the local FCN (fully convolution neural network) and YOLO-v5 (You Only Look Once-v5) to the detection of small targets in remote sensing images. Firstly, the application effects of R-CNN (Region-Convolutional Neural Network), FRCN (Fast Region-Convolutional Neural Network), and R-FCN (Region-Based-Fully Convolutional Network) in image feature extraction are analyzed after introducing the relevant region proposal network. Secondly, YOLO-v5 algorithm is established on the basis of YOLO algorithm. Besides, the multi-scale anchor mechanism of Faster R-CNN is utilized to improve the detection ability of YOLO-v5 algorithm for small targets in the image in the process of image detection, and realize the high adaptability of YOLO-v5 algorithm to different sizes of images. Finally, the proposed detection method YOLO-v5 algorithm + R-FCN is compared with other algorithms in NWPU VHR-10 data set and Vaihingen data set. The experimental results show that the YOLO-v5 + R-FCN detection method has the optimal detection ability among many algorithms, especially for small targets in remote sensing images such as tennis courts, vehicles, and storage tanks. Moreover, the YOLO-v5 + R-FCN detection method can achieve high recall rates for different types of small targets. Furthermore, due to the deeper network architecture, the YOL v5 + R-FCN detection method has a stronger ability to extract the characteristics of image targets in the detection of remote sensing images. Meanwhile, it can achieve more accurate feature recognition and detection performance for the densely arranged target images in remote sensing images. This research can provide reference for the application of remote sensing technology in China, and promote the application of satellites for target detection tasks in related fields.

SAR Target Detection Based on Domain Adaptive Faster R-CNN with Small Training Data Size

It is expensive and time-consuming to obtain a large number of labeled synthetic aperture radar (SAR) images. In the task of small training data size, the results of target detection on SAR images using deep network approaches are usually not ideal. In this study, considering that optical remote sensing images are much easier to be labeled than SAR images, we assume to have a large number of labeled optical remote sensing images and a small number of labeled SAR images with the similar scenes, propose to transfer knowledge from optical remote sensing images to SAR images, and develop a domain adaptive Faster R-CNN for SAR target detection with small training data size. In the proposed method, in order to make full use of the label information and realize more accurate domain adaptation knowledge transfer, an instance level domain adaptation constraint is used rather than feature level domain adaptation constraint. Specifically, generative adversarial network (GAN) constraint is applied as the domain adaptation constraint in the adaptation module after the proposals of Faster R-CNN to achieve instance level domain adaptation and learn the transferable features. The experimental results on the measured SAR image dataset show that the proposed method has higher detection accuracy in the task of SAR target detection with small training data size than the traditional Faster R-CNN.

Progressive structure network-based multiscale feature fusion for object detection in real-time application

Deep learning-based target detection techniques have already made a wide-range impact on our daily life. Currently, a feature pyramid is a widely utilized technique for multiscale target detection, the effectiveness of the technique has already been proved. Nevertheless, in the pyramid structure, problems, such as multiscale feature alignment, model turmoil after fusion, feature redundancy, and no-local feature fusion, exist. In this paper, we propose a novel progressive structure network to solve the aforementioned problems. The proposed structure contains three modules: multiscale feature alignment fusion, different scale channels & spatial location adaptive weighted fusion, and multiscale global and local feature fusion. The proposed structure is capable of fusing information from different feature layers more effectively. Subsequently, the semantic gaps among different scales can be reduced. Furthermore, the proposed structure can maintain the stability of the detection network and its performance has been proved by comparing with other state-of-art feature fusion method. The proposed progressive network structure has also been applied to actual target detection tasks and the practical application effectiveness of our method has been verified.

Auditory evoked brain potentials as markers of chronic effects of mild traumatic brain injury in mid-life

ObjectiveAuditory event-related potential (ERP) correlates of pre-dementia in late-life may also be sensitive to chronic effects of mild traumatic brain injury (mTBI) in mid-life. In addition to mTBI history, other clinical factors may also influence ERP measures of brain function. This study’s objective was to evaluate the relationship between mTBI history, auditory ERP metrics, and common comorbidities. MethodsERPs elicited during an auditory target detection task, psychological symptoms, and hearing sensitivity were collected in 152 combat-exposed veterans and service members, as part of a prospective observational cohort study. Participants, with an average age of 43.6 years, were grouped according to positive (n = 110) or negative (n = 42) mTBI history. Positive histories were subcategorized into repetitive mTBI (3 + ) (n = 40) or non-repetitive (1–2) (n = 70). ResultsPositive history of mTBI was associated with reduced N200 amplitude to targets and novel distractors. In participants with repetitive mTBI compared to non-repetitive and no mTBI, P50 was larger in response to nontargets and N100 was smaller in response to nontargets and targets. Changes in N200 were mediated by depression and anxiety symptoms and hearing loss, with no evidence of a supplementary direct mTBI pathway. ConclusionsAuditory brain function differed between the positive and negative mTBI groups, especially for repetitive injury, which implicated more basic, early auditory processing than did any mTBI exposure. Symptoms of internalizing psychopathology (depression and anxiety) and hearing loss are implicated in mTBI’s diminished brain responses to behaviorally relevant and novel stimuli. SignificanceA mid-life neurologic vulnerability conferred by mTBI, particularly repetitive mTBI, may be detectable using auditory brain potentials, and so auditory ERPs are a target for study of dementia risk in this population.

Orienting of covert attention by neutral and emotional gaze cues appears to be unaffected by mild to moderate amblyopia.

Amblyopia is a developmental disorder of vision associated with higher-order visual attention deficits. We explored whether amblyopia affects the orienting of covert spatial attention by measuring the magnitude of the gaze cueing effect from emotional faces. Gaze and emotion cues are key components of social attention. Participants with normal vision (n = 30), anisometropic (n = 7) or strabismic/mixed (n = 5) amblyopia performed a cued peripheral target detection task under monocular and binocular viewing conditions. The cue consisted of a centrally presented face with left or right gaze (50% validity to target location) and a fearful, happy, or neutral expression. The magnitude of spatial cueing was computed as the reaction time difference between congruent and incongruent trials for each expression. Fearful facial expressions oriented spatial attention significantly more than happy or neutral expressions. The magnitude of the gaze cueing effect in our cohort of mild-to-moderate amblyopia was comparable to that in normal vision and was not correlated with the severity of amblyopia. There were no statistical group or amblyopia subtype differences for reaction time in any viewing condition. These results place constraints on the range of attentional mechanisms affected by amblyopia and possibly suggest normal covert processing of emotional face stimuli in mild and moderate amblyopia.

Vessel Target Detection in Spaceborne–Airborne Collaborative SAR Images via Proposal and Polarization Fusion

This paper focuses on vessel detection through the fusion of synthetic aperture radar (SAR) images acquired from spaceborne–airborne collaborative observations. The vessel target detection task becomes more challenging when it features inshore interferences and structured and shaped targets. We propose a new method, based on target proposal and polarization information exploitation (TPPIE), to fuse the spaceborne–airborne collaborative SAR images for accurate vessel detection. First, a new triple-state proposal matrix (TSPM) is generated by combining the normed gradient-based target proposal and the edge-based morphological candidate map. The TSPM can be used to extract the potential target regions, as well as filtering out the sea clutter and inshore interference regions. Second, we present a new polarization feature, named the absolute polarization ratio (APR), to exploit the intensity information of dual-polarization SAR images. In the APR map, the vessel target regions are further enhanced. Third, the final fused image with enhanced targets and suppressed backgrounds (i.e., improved target-to-clutter ratio; TCR) is attained by taking the Hadamard product of the intersected TSPM from multiple sources and the composite map exploiting the APR feature. Experimental analyses using Gaofen-3 satellite and unmanned aerial vehicle (UAV) SAR imagery indicate that the proposed TPPIE fusion method can yield higher TCRs for fused images and better detection performance for vessel targets, compared to commonly used image fusion approaches.

Arousal State-Dependence of Interactions Between Short- and Long-Term Auditory Novelty Responses in Human Subjects.

In everyday life, predictable sensory stimuli are generally not ecologically informative. By contrast, novel or unexpected stimuli signal ecologically salient changes in the environment. This idea forms the basis of the predictive coding hypothesis: efficient sensory encoding minimizes neural activity associated with predictable backgrounds and emphasizes detection of changes in the environment. In real life, the brain must resolve multiple unexpected sensory events occurring over different time scales. The local/global deviant experimental paradigm examines auditory predictive coding over multiple time scales. For short-term novelty [hundreds of milliseconds; local deviance (LD)], sequences of identical sounds (/xxxxx/) are interspersed with sequences that contain deviants (/xxxxy/). Long-term novelty [several seconds; global deviance (GD)] is created using either (a) frequent /xxxxx/ and infrequent /xxxxy/ sequences, or (b) frequent /xxxxy/ and infrequent /xxxxx/ sequences. In scenario (a), there is both an LD and a GD effect (LDGD, “double surprise”). In (b), the global deviant is a local standard, i.e., sequence of identical sounds (LSGD). Cortical responses reflecting LD and GD originate in different brain areas, have a different time course, and are differentially sensitive to general anesthesia. Neural processes underlying LD and GD have been shown to interact, reflecting overlapping networks subserving the detection of novel auditory stimuli. This study examined these interactions using intracranial electroencephalography in neurosurgical patients. Subjects performed a GD target detection task before and during induction of anesthesia with propofol. Recordings were made from the auditory cortex, surrounding auditory-related and prefrontal cortex in awake, sedated, and unresponsive states. High gamma activity was used to measure the neural basis of local-by-global novelty interactions. Positive interaction was defined as a greater response to the double surprise LDGD condition compared to LSGD. Negative interaction was defined as a weaker response to LDGD. Positive interaction was more frequent than negative interaction and was primarily found in auditory cortex. Negative interaction typically occurred in prefrontal cortex and was more sensitive to general anesthesia. Temporo-parietal auditory-related areas exhibited both types of interaction. These interactions may have relevance in a clinical setting as biomarkers of conscious perception in the assessment of depth of anesthesia and disorders of consciousness.

The Attentional Blink when viewing Natural vs. Urban Scenes

The current study aimed to identify how the allocation of attention when viewing natural vs. urban scenes impacts the attentional blink. Previous research has found that natural scenes produce a broader allocation of attention, allowing attention to spread across an image and potentially use less cognitive resources. Urban scenes, on the other hand, produce a narrowed allocation of attention, potentially allowing more efficient encoding of relevant information and inhibition of irrelevant information. Participants in Experiment 1 viewed a rapid serial visual presentation (RSVP) of 16 images from either a natural or urban scene category, where two target scenes were in color and the rest of the scenes were in greyscale. An attentional blink was evident by reduced accuracy for reporting a second target that occurred two or three scenes after the first, given that the first target was accurately reported. The attentional blink was reduced for urban scenes compared to natural scenes, suggesting that the difference in scene type contributes to more efficient allocation of attention for urban scenes. To test whether this difference in the size of the attentional blink is accompanied by a difference in spread of attention between scene types, Experiment 2 used a similar RSVP task to Experiment 1 but added a peripheral target detection task. A black dot was placed around the corner of some of the images and participants reported at the end of each trial if a dot was present or not. Dots were better detected for natural scene trials, suggesting that participants have a broader spread of attention for natural scenes. The current results suggest that urban scenes lead to a narrowed attention allocation that allowed participants to more efficiently prioritize and identify targets compared to a broad spread of attention when viewing natural scenes.

Infrared dim target detection via mode-k1k2 extension tensor tubal rank under complex ocean environment

Infrared dim target detection under complex ocean environment plays a key role in military and civilian fields. Many state-of-the-art methods have disadvantages such as low generalization ability, poor robustness to noise and stubborn edges, high time complexity, and the existence of background residuals or target defects in detection results. To further solve these shortcomings, based on the infrared patch tensor (IPT) model, a robust infrared dim target detection algorithm is proposed in this paper, which converts the target detection task into a convex optimization problem. Aiming at the current situation that the tensor rank approximation in the IPT model has not been well resolved, a new vector form of tensor rank named mode-k1k2 extension tensor tubal rank (METTR) is defined, the elements of which include the tubal ranks of all tensors expanded via mode-k1k2 extension. Through the mode-k1k2 extension of the tensor, the hidden information among the different modes of the tensor is better mined. To minimize the METTR efficiently, we propose its convex approximation norm METTR, and establish a tensor robust principal component analysis (TRPCA) model joint l1 norm. Then we use the alternating direction multiplier method (ADMM) and set the optimal parameters to solve the proposed model. A series of experimental results show that the proposed algorithm outperforms the baselines in terms of background suppression and target detection.

To Grasp the World at a Glance: The Role of Attention in Visual and Semantic Associative Processing.

Associative relations among words, concepts and percepts are the core building blocks of high-level cognition. When viewing the world ‘at a glance’, the associative relations between objects in a scene, or between an object and its visual background, are extracted rapidly. The extent to which such relational processing requires attentional capacity, however, has been heavily disputed over the years. In the present manuscript, I review studies investigating scene–object and object–object associative processing. I then present a series of studies in which I assessed the necessity of spatial attention to various types of visual–semantic relations within a scene. Importantly, in all studies, the spatial and temporal aspects of visual attention were tightly controlled in an attempt to minimize unintentional attention shifts from ‘attended’ to ‘unattended’ regions. Pairs of stimuli—either objects, scenes or a scene and an object—were briefly presented on each trial, while participants were asked to detect a pre-defined target category (e.g., an animal, a nonsense shape). Response times (RTs) to the target detection task were registered when visual attention spanned both stimuli in a pair vs. when attention was focused on only one of two stimuli. Among non-prioritized stimuli that were not defined as to-be-detected targets, findings consistently demonstrated rapid associative processing when stimuli were fully attended, i.e., shorter RTs to associated than unassociated pairs. Focusing attention on a single stimulus only, however, largely impaired this relational processing. Notably, prioritized targets continued to affect performance even when positioned at an unattended location, and their associative relations with the attended items were well processed and analyzed. Our findings portray an important dissociation between unattended task-irrelevant and task-relevant items: while the former require spatial attentional resources in order to be linked to stimuli positioned inside the attentional focus, the latter may influence high-level recognition and associative processes via feature-based attentional mechanisms that are largely independent of spatial attention.

Underwater Target Detection Based on Deep Neural Network and Image Enhancement

Underwater target detection tasks refer to the detection of targets contained in underwater images. Unlike traditional target detection tasks, for underwater targets, due to factors such as illumination, camera shake, complex background interference, and diversification of target types, the effect of target detection will be affected. In this paper, we propose a target detection algorithm based on image enhancement and deep network. The algorithm first enhances the image data to obtain a better contrast, and then uses a deep learning algorithm to separate the target and the background to improve the detection performance of the target. Experimental results show that the algorithm can achieve better detection performance.

A Comparison of Auditory and Visual Representations of System Confidence to Support Trust Specificity, Attention Management, and Joint Performance in Human-Machine Teams

Breakdowns in human-robot teaming can result from trust miscalibration, i.e., a poor mapping of trust to a system’s capabilities, resulting in misuse or disuse of the technology. Trust miscalibration also negatively affects operators’ top-down attention allocation and monitoring of the system. This experiment assessed the efficacy of visual and auditory representations of a system’s confidence in its own abilities for supporting trust specificity, attention management and joint performance in the context of a UAV-supported target detection task. In contrast to earlier studies, neither visual nor auditory confidence information improved detection accuracy. Visual representations of confidence led to slower response times than auditory representations, likely due to resource competition with the visual target detection task. Finally, slower response times were observed when a UAV incorrectly detected a target. Results from this study can inform the design of visual and auditory representations of system confidence in human-machine teams with high attention demands.

An advanced YOLOv3 method for small-scale road object detection

Road target detection is a very challenging task in the field of computer vision because it is easily affected by complex backgrounds and sparse features of small targets. YOLOv3 (You Only Look Once v3) is currently one of the state-of-the-art object detection methods of deep learning. However, because the k-means clustering algorithm is sensitive to the initial clustering center, the local fragile visual field features related to small objects in the prediction map are severely lost and the final decision-making theory (The grid located in the center of the foreground object is responsible for predicting this object) of the network ignores the detailed information of the neighboring grid, there are still many problems in object detection. In this paper, we propose an improved algorithm based on YOLOv3 for small-scale object detection. We use the improved k-medians clustering method instead of the previous k-means to improve the model instability caused by the singularity; We propose a local enhancement method to strengthen weak features for small-scale object detection by paralleling a branch on the backbone. Besides, a flexible offset sampling structure added in parallel for information compensation is also designed. A series of experiments showing that our system has achieved good detection results on the KITTI and UA-DETRAC public datasets, and the distinguishing performance for small-scale objects is significantly improved. Therefore, our method is effective in road target detection tasks.

Visual perception of apparent motion abides by minimization principles of geometry.

Apparent motion is a robust perceptual phenomenon in which observers perceive a stimulus traversing the vacant visual space between two flashed stimuli. Although it is known that the "filling-in" of apparent motion favors the simplest and most economical path, the interpolative computations remain poorly understood. Here, we tested whether the perception of apparent motion is best characterized by Newtonian physics or kinematic geometry. Participants completed a target detection task while Pacmen-shaped objects were presented in succession to create the perception of apparent motion. We found that target detection was impaired when apparent motion, as predicted by kinematic geometry, not Newtonian physics, obstructed the target's location. Our findings shed light on the computations employed by the visual system, suggesting specifically that the "filling-in" perception of apparent motion may be dominated by kinematic geometry, not Newtonian physics. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Light-SAR-ShipNet (LSSNet): A Lightweight SAR Image Ship Detection System with Enhanced Deep Supervision

Applying deep learning methods to achieve synthetic aperture radar (SAR) image ship target detection has become a current hot research topic. However, most of the existing detection networks are designed based on optical images, so when they are directly applied to SAR image target detection, there will be the following two problems: 1) There are big differences between SAR image and optical image in imaging mechanism, geometric characteristics and radiation characteristics, etc, which makes the detection network have more redundant imformation; 2) Real-time performance and accuracy are both very important in SAR image target detection task, and applying a network designed based on optical image cannot balance the performance of the two well. In response to the above problems, this paper proposes a lightweight network LSSNet specially designed for SAR image ship detection task. In earlier layers, we use depthwise separable convolution instead of conventional convolution to design dense block; in deeper layers, stacked dense blocks with shortcuts are used to enhance deep supervision, and finally achieve high-speed and high-accuracy detection. This paper uses SSDD dataset as baseline for expeiment and results show that LSSNet have higher accurracy and detecition speed. Its lightweight structure will also help transplant to hardware devices in the future.

Detection for ship by dual-polarization imaging radiometer.

Passive millimeter wave (PMMW) imagers are increasingly becoming practical sensor candidates for target detection tasks. This paper is devoted to the ship detection by land-based W band passive polarized imager. The radiation characteristics of sky, sea surface and the atmospheric absorption characteristics of sea area near Qingdao are calculated based on sounding data. And the W band radiation characteristic of painted metal is measured. Then, the experiments of detection for sea surface ship are carried out in different ranges including 2,5, and 15 kilometers. The results show that land-based PMMW imager can detect and locate ship target from sea surface more than tens of kilometers away in thick fog, and polarization selection has influence on detection performance.