Visual Target Detection Research Articles

SCIENTIFIC AND METHODOLOGICAL APPROACH TO DETERMINING THE DEGREE OF DANGER OF AN AIR TARGET BY CREW OF A COMBAT VEHICLE WITH A TURRETED MACHINE GUN

In the course of hostilities in eastern Ukraine, the main threat to critical infrastructure is posed by airborne attack weapons, in particular, the “Shahed-136” kamikaze drone and its russian counterpart, the “Geranium-2”. A combat vehicle with a turreted machine gun mount, which is part of mobile fire groups, has a limited number of means that provide high-quality visual observation, detection and recognition of air targets. Therefore, the task of determining the degree of danger of a detected and recognized drone is relevant in order to make an adequate decision on its destruction. The paper considers methods for detecting and recognizing an aerial target, determining its angle and direction of movement by the human visual system using optical and electronic devices and optical means of visual observation. The article proposes a scientific and methodological approach that allows detecting and recognizing an aerial target such as a kamikaze drone and determining the degree of its danger, which minimizes the time for the commander of a combat vehicle equipped with turreted machine guns to make a quick and adequate decision on destruction.

Pre-stimulus EEG phase coherence predicts visual target detection failures in schizophrenia: A pilot study

Impaired visual target detection is a common finding in schizophrenia that is linked to poor functional outcomes. However, the neural mechanisms that contribute to this deficit remain unclear. Recent research in healthy samples has identified relationships between the phase of pre-stimulus electroencephalographic (EEG) activity in the alpha band (8-12 Hz) or theta band (4-7 Hz) and the likelihood of visual target detection with and without attentional cueing, but these effects have not yet been explored in schizophrenia. We performed a study to investigate such effects in schizophrenia (n = 19) and healthy participants (n = 14), using a visual target detection task with attentional cues. We found significant relationships between pre-stimulus EEG phase properties and visual target detection in both groups, but also clear differences in the effects as a function of frequency, group, and attentional cueing. Alpha-band phase effects were relatively uniform across groups and conditions. By contrast, theta-band phase effects showed differences by group and attentional condition which could be consistent with attentional hyperfocusing in the schizophrenia group. Thus, our results elucidate a novel neural mechanism that may help to explain known impairments affecting both visual target detection and attention in schizophrenia.

Copper (II)-catalyzed polydopamine mediated photothermal sensors for visual quantitative point-of-care testing

BackgroundTemperature sensing is commonly used in point-of-care (POC) detection technologies, yet the portability and convenience of use are frequently compromised by the complexity of thermosensitive processes and signal transduction. Especially, multi-step target recognition reactions and temperature measurement in the reaction vessel present challenges in terms of stability and integration of detection devices. To further combine photothermal reaction and signal readout in one assay, these two processes enable to be integrated into miniaturized microfluidic chips, thereby facilitating photothermal sensing and achieving a simple visual temperature sensing as POC detection. ResultsA copper ion (Cu2+)-catalyzed photothermal sensing system integrated onto a microfluidic distance-based analytical device (μDAD), enabling the visual, portable, and sensitive quantitative detection of multiple targets, including ascorbic acid, glutathione, and alkaline phosphatase (ALP). The polydopamine nanoparticles (PDA NPs) were synthesized by the regulation of free Cu2+ through redox or coordination reactions, facilitating the transduction of distinct photothermal response signals and providing the versatile Cu2+-responsive sensing systems. Promoted by integration with a photothermal μDAD, the system combines PDA's photothermal responsiveness and thermosensitive gas production of ammonium bicarbonate for improved sensitivity of ALP detection, reaching the detection limit of 9.1 mU/L. The system has successfully achieved on-chip detection of ALP with superior anti-interference capability and recoveries ranging from 96.8 % to 104.7 %, alongside relative standard deviations below 8.0 %. Significance and noveltyThe μDAD design accommodated both the photothermal reaction of PDA NPs and thermosensitive gas production reaction, achieving the rapid sensing of visual distance signals. The μDAD-based Cu2+-catalyzed photothermal sensing system holds substantial potential for applications in biochemical analysis and clinical diagnostics, underscored by the versatile Cu2+ regulation mechanism for a broad spectrum of biomarkers.

Benchmarking automation-aided performance in a forensic face matching task

Carragher and Hancock (2023) investigated how individuals performed in a one-to-one face matching task when assisted by an Automated Facial Recognition System (AFRS). Across five pre-registered experiments they found evidence of suboptimal aided performance, with AFRS-assisted individuals consistently failing to reach the level of performance the AFRS achieved alone. The current study reanalyses these data (Carragher and Hancock, 2023), to benchmark automation-aided performance against a series of statistical models of collaborative decision making, spanning a range of efficiency levels. Analyses using a Bayesian hierarchical signal detection model revealed that collaborative performance was highly inefficient, falling closest to the most suboptimal models of automation dependence tested. This pattern of results generalises previous reports of suboptimal human-automation interaction across a range of visual search, target detection, sensory discrimination, and numeric estimation decision-making tasks. The current study is the first to provide benchmarks of automation-aided performance in the one-to-one face matching task.

Encoding images to 3D by sequential model with single target feature extractor

Assisted driving is a necessary way to realize autonomous driving, in which bird's-eye-view (BEV) is an ideal solution to perceive the targets around the body, i.e., the information acquired by the sensors of the body is extracted and semantic features are integrated into the BEV plane for downstream tasks such as target detection, scene segmentation, and path planning, etc. BEV-based pure vision target detection refers to the use of ordinary cameras without relying on other sensors to perceive the targets around the body. BEV-based pure visual target detection, on the other hand, refers to the use of ordinary cameras to perceive targets around the body without relying on other sensors, and Lift Splat Shoot (LSS) is a more typical solution among the existing schemes. Since the information obtained by each camera in assisted driving is always continuous, incorporating the temporal information into the model can achieve better detection results. We design the model (Sequential and Single Target based LSS) SSTL, and the experiment proves that our model has a certain performance improvement based on the original model.

Performance of cone-beam computed tomography imaging during megavoltage beam irradiation under phase-gated conditions

PurposeTarget positions should be acquired during beam delivery for accurate lung stereotactic body radiotherapy. We aimed to perform kilovoltage (kV) imaging during beam irradiation (intra-irradiation imaging) under phase-gated conditions and evaluate its performance. MethodsCatphan 504 and QUASAR respiratory motion phantoms were used to evaluate image quality and target detectability, respectively. TrueBeam STx linac and the Developer Mode was used. The imaging parameters were 125 kVp and 1.2 mAs/projection. Flattened megavoltage (MV) X-ray beam energies 6, 10 and 15 MV and un-flattened beam energies 6 and 10 MV were used with field sizes of 5 × 5 and 15 × 15 cm2 and various frame rates for intra-irradiation imaging. In addition, using a QUASAR phantom, intra-irradiation imaging was performed during intensity-modulated plan delivery. The root-mean-square error (RMSE) of the CT-number for the inserted rods, image noise, visual assessment, and contrast-to-noise ratio (CNR) were evaluated. ResultsThe RMSEs of intra-irradiation cone-beam computed tomography (CBCT) images under gated conditions were 50–230 Hounsfield Unit (HU) (static < 30 HU). The noise of the intra-irradiation CBCT images under gated conditions was 15–35 HU, whereas that of the standard CBCT images was 8.8–27.2 HU. Lower frame rates exhibited large RMSEs and noise; however, the iterative reconstruction algorithm (IR) was effective at improving these values. Approximately 7 fps with the IR showed an equivalent CNR of 15 fps without the IR. The target was visible on all the gated intra-irradiation CBCT images. ConclusionSeveral image quality improvements are required; however, intra-irradiated CBCT images showed good visual target detection.

An Investigation of a Biomimetic Optical System and an Evaluation Model for the Qualitative Analysis of Laser Interference Visual Levels.

To objectively quantify the level of visual interference induced by lasers, we developed a biomimetic optical system designed to emulate human vision. This system is based on an optical model of the eye and synthetic imaging principles, allowing it to generate biomimetic optical images that closely mimic human visual perception. Upon exposure to a 532 nm laser, biomimetic optical images were captured under various ambient lighting conditions. By employing a contrast threshold model for human visual target detection and grayscale hierarchy analysis, we devised an evaluation model to quantify the levels of laser-induced visual interference. The bionic images obtained from our experiments, in conjunction with the constructed model, enabled us to assess the degree of laser-induced visual interference. Our results indicate that this system can effectively substitute the human eye when testing laser imaging effects, with the generated bionic images achieving up to 90% concordance with human vision. The proposed evaluation model facilitates the quantitative analysis of laser-induced visual impairment. This apparatus and evaluation model hold significant promise for the precise quantification of laser-induced visual interference levels.

Robotic Odor Source Localization via Vision and Olfaction Fusion Navigation Algorithm.

Robotic odor source localization (OSL) is a technology that enables mobile robots or autonomous vehicles to find an odor source in unknown environments. An effective navigation algorithm that guides the robot to approach the odor source is the key to successfully locating the odor source. While traditional OSL approaches primarily utilize an olfaction-only strategy, guiding robots to find the odor source by tracing emitted odor plumes, our work introduces a fusion navigation algorithm that combines both vision and olfaction-based techniques. This hybrid approach addresses challenges such as turbulent airflow, which disrupts olfaction sensing, and physical obstacles inside the search area, which may impede vision detection. In this work, we propose a hierarchical control mechanism that dynamically shifts the robot's search behavior among four strategies: crosswind maneuver, Obstacle-Avoid Navigation, Vision-Based Navigation, and Olfaction-Based Navigation. Our methodology includes a custom-trained deep-learning model for visual target detection and a moth-inspired algorithm for Olfaction-Based Navigation. To assess the effectiveness of our approach, we implemented the proposed algorithm on a mobile robot in a search environment with obstacles. Experimental results demonstrate that our Vision and Olfaction Fusion algorithm significantly outperforms vision-only and olfaction-only methods, reducing average search time by 54% and 30%, respectively.

Portable tri-color ratiometric fluorescence paper sensor for intelligent visual detection of dual-antibiotics and aluminium ion

The toxicological effect between co-existed antibiotics and metal ions was dangerous to the ecological environment and public health. However, the rapid quantification tools with convenience, accuracy and low cost for the detection of multiple targets were still challenging. Herein, a portable tri-color ratiometric fluorescence paper sensor was constructed by coupling of blue carbon dots and fluorescence imprinted polymer for down/up conversion simultaneous detection of tetracycline and sulfamethazine. Interestingly, the cascade detection of aluminum ion was also realized based on the individual detection system of tetracycline without the assistance of complex coupling reagents. The detection limits of smartphone method for the visual detection of tetracycline, sulfamethazine and aluminum ion were calculated as 0.014 μM, 0.004 μM and 0.019 μM, respectively. The portable fluorescence paper sensor was applied for the visual detection of tetracycline, sulfamethazine and aluminum ion in actual samples successfully with satisfactory recoveries. With the advantages of rapidness, low cost, and portability, the developed portable fluorescence paper sensor provided a new strategy for the visual real-time detection of multiple targets.

PAM-FOG Net: A Lightweight Weed Detection Model Deployed on Smart Weeding Robots

Visual target detection based on deep learning with high computing power devices has been successful, but the performance in intelligent agriculture with edge devices has not been prominent. Specifically, the existing model architecture and optimization methods are not well-suited to low-power edge devices, the agricultural tasks such as weed detection require high accuracy, short inference latency, and low cost. Although there are automated tuning methods available, the search space is extremely large, using existing models for compression and optimization greatly wastes tuning resources. In this article, we propose a lightweight PAM-FOG net based on weed distribution and projection mapping. More significantly, we propose a novel model compression optimization method to fit our model. Compared with other models, PAM-FOG net runs on smart weeding robots supported by edge devices, and achieves superior accuracy and high frame rate. We effectively balance model size, performance and inference speed, reducing the original model size by nearly 50%, power consumption by 26%, and improving the frame rate by 40%. It shows the effectiveness of our model architecture and optimization method, which provides a reference for the future development of deep learning in intelligent agriculture.

Development and clinical application of loop-mediated isothermal amplification combined with lateral flow assay for rapid diagnosis of SARS-CoV-2

BackgroundThe diagnostic assay leveraging multiple reverse transcription loop-mediated isothermal amplification (RT-LAMP) could meet the requirements for rapid nucleic acid detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).MethodsThe devised assay merged the lateral flow assay with the RT-LAMP technology and designed specific primers for the simultaneous detection of the target and human-derived internal reference genes within a single reaction. An inquiry into the assay's limit of detection (LOD), sensitivity, and specificity was carried out. The effectiveness of this assay was validated using 498 clinical specimens.ResultsThis LOD of the assay was determined to be 500 copies/mL, and there was no observed cross-reaction with other respiratory pathogens. The detection results derived from clinical specimens showed substantial concordance with those from real-time reverse transcription-polymerase chain reaction (RT-qPCR) (Cohen's kappa, 0.876; 95% CI: 0.833-0.919; p<0.005). The diagnostic sensitivity and specificity were 87.1% and 100%, respectively.ConclusionThe RT-LAMP assay, paired with a straightforward and disposable lateral immunochromatographic strip, achieves visual detection of dual targets for SARS-CoV-2 immediatly. Moreover, the entire procedure abstains from nucleic acids extraction. The samples are lysed at room temperature and subsequently proceed directly to the RT-LAMP reaction, which can be executed within 30 minutes at a constant temperature of 60-65°C. Then, the RT-LAMP amplification products are visualized using colloidal gold test strips.Trial registrationThis study was registered at the Chinese Clinical Trial Registry (Registration number: ChiCTR2200060495, Date of registration 2022-06-03).

Universal smartphone-assisted label-free CRISPR/Cas12a-DNAzyme chemiluminescence biosensing platform for on-site detection of nucleic acid and non-nucleic acid targets

The clustered regularly interspaced short palindromic repeats (CRISPR)-associated protein (Cas) (CRISPR/Cas) system enables sensitive and specific detection of biomolecules, thanks to its programmability, high fidelity, and powerful signal amplification capabilities. Herein, a universal smartphone-assisted label-free G-quadruplex (G4) DNAzyme-based chemiluminescence CRISPR/Cas12a biosensing platform (G4CLCas) is firstly described that achieves on-site, ultrasensitive visual detection of nucleic acid and non-nucleic acid targets. The G4CLCas-based sensing platform relies on Cas12a trans-cleavage activation that triggers the cleavage of the G4 DNAzyme, resulting in chemiluminescence signals off/on compared to that of the control. Chemiluminescence signals are captured as images that are quantitatively analyzed and visualized using a smartphone-assisted imaging cartridge. Under optimal conditions, G4CLCas achieves a low limit of detection (LOD) of 8.6 aM (∼5.2 copies/μL) for monkeypox virus (MPXV) DNA within the linear concentration range of 10–300 aM and can accurately quantify viral DNA in spiked samples. G4CLCas can also detect non-nucleic acid targets, whereby it achieves a low LOD value of 84.3 nM for adenosine triphosphate (ATP) within the linear concentration range of 2–2000 μM. Here, a label-free, portable, on-site CRISPR/Cas12a chemiluminescence biosensing platform based on the G4 DNAzyme substrates is proposed with potential applications in clinical detection and bioanalytical chemistry research.

Light-Driven Photocatalytic-Photothermal Synergetic System for Portable and Sensitive Nucleic Acid Quantification.

Photosensitizers and photothermal agents have attracted increasing attention for in vitro diagnosis, but the combination remains challenging. Herein, a light-driven photocatalytic-photothermal synergetic system integrated microfluidic distance-based analytical device (PCPT-μDAD) for visual, portable, sensitive, and quantitative detection of targets was developed. Target DNA was recognized and initiated the hybridization chain reaction to form a double-stranded DNA/SYBR Green I (dsDNA/SG-I) complex. By applying the photosensitization of the dsDNA/SG-I complex and the photothermal effect of oxidized 3,3',5,5'-tetramethylbenzidine, the target concentration can effectively translate into a visual distance signal readout. Importantly, the light-driven PCPT-μDAD greatly improves the controllability of catalytic reactions and signal amplification efficiency. The light-driven PCPT-μDAD shows a low limit of detection (fM level), good stability, and high reproducibility for nucleic acid detection.

Extraction and analysis of factors influencing college students’ mental health based on deep learning model

Abstract This paper analyzes the factors that affect the mental health of college students, and the focus of this analysis is on emotional-emotional factors. The extroverted presentation of emotional affects is used as visual information to study the mental health status of college students. Based on the advantages of long and short memory neural networks based on deep learning models in processing two-dimensional images, a computer vision task is used to perform visual recognition, target detection, and expression image classification of college students’ facial expressions. The use of video facial expression recognition with multi-feature fusion is utilized to effectively identify the facial expression machine of college students in both laboratory-controlled and outdoor environments. The mental health status of college students was analyzed in terms of facial expression recognition and feature extraction. The recognition rate for general features was 80.3%, 89.3% for six specific facial emotions, and 84.4% for LBP features.

Descending neurons of the hoverfly respond to pursuits of artificial targets

Many animals use motion vision information to control dynamic behaviors. Predatory animals, for example, show an exquisite ability to detect rapidly moving prey, followed by pursuit and capture. Such target detection is not only used by predators but is also important in conspecific interactions, such as for male hoverflies defending their territories against conspecific intruders. Visual target detection is believed to be subserved by specialized target-tuned neurons found in a range of species, including vertebrates and arthropods. However, how these target-tuned neurons respond to actual pursuit trajectories is currently not well understood. To redress this, we recorded extracellularly from target-selective descending neurons (TSDNs) in male Eristalis tenax hoverflies. We show that they have dorso-frontal receptive fields with a preferred direction up and away from the visual midline. We reconstructed visual flow fields as experienced during pursuits of artificial targets (black beads). We recorded TSDN responses to six reconstructed pursuits and found that each neuron responded consistently at remarkably specific time points but that these time points differed between neurons. We found that the observed spike probability was correlated with the spike probability predicted from each neuron's receptive field and size tuning. Interestingly, however, the overall response rate was low, with individual neurons responding to only a small part of each reconstructed pursuit. In contrast, the TSDN population responded to substantially larger proportions of the pursuits but with lower probability. This large variation between neurons could be useful if different neurons control different parts of the behavioral output.

Frontocentral EEG activity phase predicts subsequent visual target detection in healthy participants but not in schizophrenia

Frontocentral EEG activity phase predicts subsequent visual target detection in healthy participants but not in schizophrenia

The role of middle frontal gyrus in working memory retrieval by the effect of target detection tasks: a simultaneous EEG-fMRI study.

Maintained working memory (WM) representations have been shown to influence visual target detection selection, while the effect of the visual target detection process on WM retrieval remains largely unknown. In the current research, we used the dual-paradigm of the visual target detection task and the delayed matching task (DMT), which contained the following four conditions: the match condition: the DMT target contained the detection target; the mismatch condition: the DMT target contained the detection distractor; the neutral condition: only the detection target was presented; the catch condition: only the DMT target was presented. Twenty-six subjects were recruited in the experiment with simultaneous EEG-fMRI data. Behaviorally, faster responses were found in the mismatch condition than in the match and neutral conditions. The EEG data found a greater parieto-occipital N1 component in the mismatch condition compared to the neutral condition, and a greater frontal N2 component in the match condition than in the mismatch condition. Moreover, compared to the match and neutral conditions, weaker activations of the bilateral middle frontal gyrus (MFG) were observed in the mismatch condition. And the representational similarity analysis (RSA) revealed significant differences in the representational patterns of the bilateral MFG between mismatch and match conditions, as well as in the representational patterns of the left MFG between mismatch and neutral conditions. Additionally, the left MFG may be the brain source of the N1 component in the mismatch condition. These findings suggest that the mismatch between the DMT target and detection target affects early attention allocation and attentional control in WM retrieval, and the MFG may play an important role in WM retrieval by the effect of the target detection task. In conclusion, our work deepens the understanding of the neural mechanisms by which visual target detection affects WM retrieval.

The mismatch negativity to abstract relationship of tone pairs is independent of attention

The mismatch negativity (MMN) implicating a comparison process between the deviant and the memory trace of the standard can be elicited by not only changes in physical features but also violations of abstract patterns. It is considered pre-attentive, yet the use of the passive design makes it difficult to exclude the possibility of attention leak. In contrast to how this issue has been well addressed with the MMN to physical changes, much less research directly investigated the attentional effect on the MMN to abstract relationships. Here we conducted an electroencephalography (EEG) experiment to study whether and how the MMN to abstract relationships is modulated by attention. We adapted the oddball paradigm of Kujala et al. by presenting occasional descending tone pairs among frequent ascending tone pairs, while additionally implementing a novel control of attention. Participants’ attention was either directed away from the sounds (with an engaging task of visual target detection, so that the sounds were task-irrelevant) or toward the sounds (with a conventional task of auditory deviant detection, so that the sounds were task-relevant). The MMN to abstract relationships appeared regardless of attention, confirming the pre-attentive assumption. The attention-independence of the frontocentral and supratemporal components of the MMN supported the notion that attention is not required to generate the MMN. At the individual level, a relatively equal number of participants showed attention enhancement and attention suppression. It is unlike the attentional modulation on the P3b, which was robustly elicited in the attended condition only. The concurrent collection of these two neurophysiological markers in both unattended and attended conditions might be potentially suitable for testing clinical populations showing heterogeneous deficits in auditory function independent/dependent of attention.

Tool use moves the peri-personal space from the hand to the tip of the tool.

In this study, we used a visual target detection task to investigate three hypotheses about how the peri-personal space is extended after tool-use training: Addition, Extension, and Projection hypotheses. We compared the target detection performance before and after tool-use training. In both conditions, the participants held a hockey stick-like tool in their hands during the detection task. Furthermore, we added the no-tool-holding condition to the experimental design. In the no-tool-holding condition, a peri-hand space advantage in the visual target detection task was observed. When the participants held the tool with their hands, this peri-hand space advantage was lost. Furthermore, there was no peri-tool space advantage before tool training. After tool training, the peri-tool space advantage was observed. However, after tool training, the advantage of the peri-hand space was not observed. This result suggested that the peri-hand advantage was reduced by simply holding the tool because the participants lost the functionality of their hands. Furthermore, tool-use training improved detection performance only in the peri-tool space. Thus, these results supported the projection hypothesis that the peri-personal space advantage would move from the body to the functional part of the tool.

Deviance Distraction and Stimulus-Specific Adaptation in the Somatosensory Cortex Reduce with Experience.

Automatic detection of a surprising change in the sensory input is a central element of exogenous attentional control. Stimulus-specific adaptation (SSA) is a potential neuronal mechanism detecting such changes and has been robustly described across sensory modalities and different instances of the ascending sensory pathways. However, little is known about the relationship of SSA to perception. To assess how deviating stimuli influence target signal detection, we used a behavioral cross-modal paradigm in mice and combined it with extracellular recordings from the primary somatosensory whisker cortex. In this paradigm, male mice performed a visual detection task while task-irrelevant whisker stimuli were either presented as repetitive "standard" or as rare deviant stimuli. We found a deviance distraction effect on the animals' performance: Faster reaction times but worsened target detection was observed in the presence of a deviant stimulus. Multiunit activity and local field potentials exhibited enhanced neuronal responses to deviant compared with standard whisker stimuli across all cortical layers, as a result of SSA. The deviant-triggered behavioral distraction correlated with these enhanced neuronal deviant responses only in the deeper cortical layers. However, the layer-specific effect of SSA on perception reduced with increasing task experience as a result of statistical distractor learning. These results demonstrate a layer-specific involvement of SSA on perception that is susceptible to modulation over time.SIGNIFICANCE STATEMENT Detecting sudden changes in our immediate environment is behaviorally relevant and important for efficient perceptual processing. However, the connection between the underpinnings of cortical deviance detection and perception remains unknown. Here, we investigate how the cortical representation of deviant whisker stimuli impacts visual target detection by recording local field potential and multiunit activity in the primary somatosensory cortex of mice engaged in a cross-modal visual detection task. We find that deviant whisker stimuli distract animals in their task performance, which correlates with enhanced neuronal responses for deviants in a layer-specific manner. Interestingly, this effect reduces with the increased experience of the animal as a result of distractor learning on statistical regularities.