Precise Target Localization Research Articles

High precision 3D reconstruction and target location based on the fusion of visual features and point cloud registration

High precision 3D reconstruction and target location based on the fusion of visual features and point cloud registration

Research and Experiment on Miss-Seeding Detection of Potato Planter Based on Improved YOLOv5s

In order to improve the performance of potato planter, reduce miss-seeding rates, enhance the overall quality of the seeding operation, and ultimately increase the yield of the potato, it is necessary to implement effective technical means to monitor and identify the miss-seeding issues during the seeding process. The existing miss-seeding detection technologies commonly use sensors to monitor, but such technologies are easily affected by factors like heavy dust and strong vibrations, resulting in poor interference resistance and adaptability. Therefore, this study aims to explore and apply deep learning algorithms to achieve real-time monitoring of the miss-seeding phenomenon in potato planter during the planting process. Considering both the lightweight of the miss-seeding detection model and its practical deployment, this study selects and adapts the YOLOv5s algorithm to achieve this goal. Firstly, the attention mechanism is integrated into the backbone network to suppress background interference and improve detection accuracy. Secondly, the non-maximum suppression algorithm is improved by replacing the original IoU-NMS with the Soft-NMS algorithm to enhance the bounding box regression rate and reduce missed detections of potato seeds due to background overlap or occlusion. Experimental results show that the accuracy of the improved algorithm in detecting miss-seeding increased from 96.02% to 98.30%, the recall rate increased from 96.31% to 99.40%, and the mean average precision (mAP) improved from 99.12% to 99.40%. The improved model reduces missed and false detections, provides more precise target localization, and is suitable for miss-seeding detection in natural environments for potato planter, providing technical and theoretical support for subsequent intelligent reseeding in potato planter.

An Underwater Target Location Algorithm Combined with Schmidt Orthogonalization

Abstract In underwater target localization research, where challenges such as low signal-to-noise ratio and multiple interferences exist in conventional matched field processing, this paper presents a novel approach combining compressed sensing with Schmidt orthogonalization. Initially, an orthogonalization procedure is applied to the observation matrix, yielding a new matrix with reduced column coherence and maximal adherence to the Restricted Isometry Property (RIP) constraint. Subsequently, the Sparse Adaptive Matching Pursuit (SAMP) algorithm from compressed sensing is employed to reconstruct the signal, leading to precise underwater target localization. Simulation experiments substantiate that this combined approach outperforms the conventional matched field algorithm by yielding a higher signal-to-noise ratio for target acoustic source localization and markedly reducing the count of interference virtual sources. Consequently, the performance of localization for targets is substantially enhanced.

Dual-branch collaborative Siamese network for visual tracking

This paper introduces a dual-branch collaborative Siamese network architecture, CoSiNet, for visual object tracking. The network has a shallow branch for precise target localization and a deep branch for extracting rich semantic information. It integrates two specialized modules - Channel Attention and Spatial Channel Attention Feature Enhancement - for improved feature extraction and background noise reduction. An Adaptive Fusion Module combines response maps from both branches to create an enriched final response map. Experimental results show our algorithm outperforms several contemporary techniques on four different public datasets (OTB100, OTB50, DTB70, TColor128), demonstrating the competitiveness of CoSiNet.

Clustered remote sensing target distribution detection aided by density-based spatial analysis

Small target detection in remote sensing is integral to a range of applications, including smart city systems and emergency rescue operations. However, the challenges posed by weak features and complex backgrounds in remote sensing images have hindered the efficacy of detection. Current models tend to focus on identifying individual targets, resulting in algorithms with larger parameters, slower detection efficiency, and difficulty in striking a balance between false positives and negatives. Given that many tasks do not require precise target location, a more efficient approach involves swiftly predicting target areas with models involving fewer parameters. This paper introduces the concept of group target distribution detection, gathering targets with similar distances and semantic similarities for clustered detection. A Gaussian probability map, formed from target density, is used to train a probability prediction model. We propose a new metric for evaluating this innovative group target distribution detection paradigm and provide a comparative assessment with traditional single-object detection models. In experimental evaluation, our proposed DenseUGE network — employing ResNet34 and ResNet50 as its backbone — surpasses the best baseline method by 3.37% on the AI-TOD dataset using our metrics. Additionally, visualizations demonstrate the ability of our proposed methodology to effectively identify the concentrated distribution of small target groups.

A lightweight ground crack rapid detection method based on semantic enhancement

To address the issue of detecting complex-shaped cracks that rely on manual, which may result in high costs and low efficiency, this paper proposed a lightweight ground crack rapid detection method based on semantic enhancement. Firstly, the introduction of the Context Guided Block module enhanced the YOLOv8 backbone network, improving its feature extraction capability. Next, the incorporation of GSConv and VoV-GSCSP was introduced to construct a lightweight yet efficient neck network, facilitating the effective fusion of information from multiple feature maps. Finally, the detection head achieved more precise target localization by optimizing the probability around the labels. The proposed method was validated through experiments on the public dataset RDD-2022. The experimental results demonstrate that our method effectively detects cracks. Compared to YOLOv8, the model parameters have been reduced by 73.5 %, while accuracy, F1 score, and FPS have improved by 6.6 %, 4.3 %, and 116, respectively. Therefore, our proposed method is more lightweight and holds significant application value.

Multi-Feature Fusion Recognition and Localization Method for Unmanned Harvesting of Aquatic Vegetables

The vision-based recognition and localization system plays a crucial role in the unmanned harvesting of aquatic vegetables. After field investigation, factors such as illumination, shading, and computational cost have become the main difficulties restricting the identification and positioning of Brasenia schreberi. Therefore, this paper proposes a new lightweight detection method, YOLO-GS, which integrates feature information from both RGB and depth images for recognition and localization tasks. YOLO-GS employs the Ghost convolution module as a replacement for traditional convolution and innovatively introduces the C3-GS, a cross-stage module, to effectively reduce parameters and computational costs. With the redesigned detection head structure, its feature extraction capability in complex environments has been significantly enhanced. Moreover, the model utilizes Focal EIoU as the regression loss function to mitigate the adverse effects of low-quality samples on gradients. We have developed a data set of Brasenia schreberi that covers various complex scenarios, comprising a total of 1500 images. The YOLO-GS model, trained on this dataset, achieves an average accuracy of 95.7%. The model size is 7.95 MB, with 3.75 M parameters and a 9.5 GFLOPS computational cost. Compared to the original YOLOv5s model, YOLO-GS improves recognition accuracy by 2.8%, reduces the model size and parameter number by 43.6% and 46.5%, and offers a 39.9% reduction in computational requirements. Furthermore, the positioning errors of picking points are less than 5.01 mm in the X direction, 3.65 mm in the Y direction, and 1.79 mm in the Z direction. As a result, YOLO-GS not only excels with high recognition accuracy but also exhibits low computational demands, enabling precise target identification and localization in complex environments so as to meet the requirements of real-time harvesting tasks.

Visual tracking with pyramidal feature fusion and transformer based model predictor

Discriminative correlation filters (DCF) have achieved much success in visual tracking. However, most of them simply rely on the features extracted by the last layer of the backbone, while ignoring the low-level rich structural information. In addition, they normally minimize the tailored objective functions to predict the target model in a direct way, which introduces inductive bias and limits the expressivity of the trackers. In view of this, a pyramidal feature fusion module is proposed in this paper to integrate the low-resolution, semantically strong features with high-resolution, semantically weak features. Then, an asymmetric Transformer structure is applied to predict the weights of the model. Finally, a feature refinement module is employed to optimize the search features. Extensive experiments on 5 mainstream datasets demonstrate the superiority of our tracker, where it has achieved better feature expression and more precise target localization.

Enhancing underwater target localization through proximity-driven recurrent neural networks

Environmental monitoring, ocean research, and underwater exploration are just a few of the marine applications that require precise underwater target localization. This study goes into the field of underwater target localization using Recurrent Neural Networks (RNNs) enhanced with proximity-based approaches, with a focus on mean estimation error as a performance metric. In complex and dynamic underwater environments, conventional localization systems frequently face challenges such as signal degradation, noise interference, and unstable hydrodynamic conditions. This paper presents a novel approach to employing RNNs to increase the accuracy of underwater target localization by exploiting the temporal dynamics of proximity-informed data. This method uses an RNN architecture to track changes in audio emissions from underwater targets sensed by a microphone network. Using the temporal correlations represented in the data, the RNN learns patterns indicative of target localization quickly and correctly. Furthermore, the addition of proximity-based features increases the model's ability to understand the relative distances between hydrophone nodes and the target, resulting in more accurate localization estimates. To evaluate the suggested methodology, thorough simulations and practical experiments were carried out in a variety of underwater environments. The results show that the RNN-based strategy beats conventional methods and works effectively even in difficult settings. The utility of the proximity-aware RNN model is demonstrated, in particular, by considerable reductions in the mean estimate error (MEE), an important performance measure.

Drone Detection and Tracking System Based on Fused Acoustical and Optical Approaches

The increasing popularity of small drones has stressed the urgent need for an effective drone‐oriented surveillance system that can work day and night. Herein, an acoustic and optical sensor‐fusion‐based system‐termed multimodal unmanned aerial vehicle 3D trajectory exposure system (MUTES) is presented to detect and track drone targets. MUTES combines multiple sensor modules including microphone array, camera, and lidar. The 64‐channel microphone array provides semispherical surveillance with high signal‐to‐noise ratio of sound source estimation, while the long‐range lidar and the telephoto camera are capable of subsequent precise target localization in a narrower but higher definition field of view. MUTES employs a coarse‐to‐fine, passive‐to‐active localization strategy for wide‐range detection (semispherical) and high‐precision 3D tracking. To further increase the fidelity, an environmental denoising model is trained, which helps to select valid acoustic features from a drone target, thus overcomes the drawbacks of the traditional sound source localization approaches when facing noise interference. The effectiveness of the proposed sensor‐fusion approach is validated through field experiments. To the best of the knowledge, MUTES provides the farthest detection range, highest 3D position accuracy, strong anti‐interference capability, and acceptable cost for unverified drone intruders.

Treatment of vocal tremor with bilateral magnetic resonance imaging-guided focused ultrasound of the ventral intermediate thalamic nucleus: illustrative case.

Essential vocal tremor is a difficult disease entity to treat with a poor response to existing medical management and limited options for surgical management of the disease. Magnetic resonance imaging-guided focused ultrasound (MRgFUS) is an emerging treatment modality with encouraging results for limb tremor in patients with essential tremor, but data are limited for the treatment of vocal tremor. This is the case of a 69-year-old male with a history of essential vocal tremor severely limiting his ability to perform his occupation as an opera singer. He underwent staged bilateral ventral intermediate nucleus of the thalamus thalamotomy with MRgFUS for the treatment of his bilateral upper extremity tremor with near complete resolution of his vocal tremor after a second procedure. Bilateral MRgFUS may be a safe and efficacious option for the treatment of essential vocal tremor. Further research into optimal patient selection, precise target location, and treatment parameters is needed.

A retrospective multi-center feasibility study of a new PTV margin estimation approach for moving targets using CyberKnife log files.

This study investigates a new approach for estimating the planning target volume (PTV) margin for moving tumors treated with robotic stereotactic body radiotherapy (SBRT). In this new approach, the covariance of modeling and prediction errors was estimated using error propagation and implemented in the Van Herk formula to form a Modified Van Herk formula (MVHF). To perform a retrospective multi-center analysis, the MVHF was studied using 163 patients treated with different system versions of robotic SBRT (G3 version 6.2.3, VSI version 8.5, and VSI version 9.5) and compared with two established PTV margins estimation methods: The original Van Herk Formula (VHF) and the Uncertainty Estimation Method (UEM). Overall, the PTV margins provided by the three formalisms are similar with 4-5mm in the lung region and 4mm in abdomen region to the PTV margins used in clinical. Furthermore, when analyzing individual patients, a difference of up to 1mm was found between the PTV margin estimations using MVHF and VHF. The corresponding average discrepancies for the superior-inferior (SI) direction ranged between -0.19mm to 0.38mm in CK G3 version 6.2.3, -0.36mm to 0.33mm in CK VSI version 8.5, and -0.34mm to 0.40mm in CK VSI version 9.5. It was found that for the lower left lung, upper left lung, lower right lung, upper right lung, central liver, and upper liver, the effect of covariance between model and prediction errors in SI direction was around 20%, 30%, 25%, 25%, 25%, and 30%, respectively. Notable covariance effects between model and prediction errors can be considered in PTV margin estimation using a modified VHF, which allowed for more precise target localization in robotic SBRT for moving tumors. Overall, in each of the three directions, the difference between MVHF and utilized clinical margins is 0.65mm in the lung and abdominal region. Therefore, to improve the clinical PTV margins with the new approach, it is suggested to use the adaptive PTV margins in the next fractions.

Clinical Practice Guidelines for the Therapeutic Use of Repetitive Transcranial Magnetic Stimulation in Neuropsychiatric Disorders.

Clinical Practice Guidelines for the Therapeutic Use of Repetitive Transcranial Magnetic Stimulation in Neuropsychiatric Disorders.

A Novel 3D Indoor Node Localization Technique Using Weighted Least Square Estimation with Oppositional Beetle Swarm Optimization Algorithm

In this research work an effective indoor localization mechanism has been implemented for smart devices, advancements of mobile-Internet and multimedia applications. Indoor localization is one of recent technology, it is a location-based service (LBS), this work has been facilitated to commercial and civilian industries. The LSB can useful in many tools such as public security, disaster management, and positioning navigation. Several research works have been concentrated on design of accurate 2D indoor localization techniques. Since 3D indoor localization techniques offer numerous benefits compared to 2D model. In this investigation a Novel 3D Indoor Node Localization Technique has been proposed using Oppositional Beetle Swarm Optimization with Weighted Least Square Estimation (OBSO-WLSE) algorithm. The proposed OBSO- WLSE algorithm aims to develop the localization accuracy with reduced computational time. The OBSO algorithm is employed for approximating initial locations of the targets, these results can minimize NLOS error. The precise location of target has been identified through WLSE technique as well as OBSO can predict initial location. To improve the efficiency of the OBSO technique, the concept of oppositional based learning (OBL) is integrated into the traditional BSO algorithm. The designed model prototype simulation has been run on MATLAB software with NS3 Tool Box. The measures like accuracy 98.45%, sensitivity 96.34%, recall 94.67%, 3D indoor localization detection rate 19.25% improvement and throughput 97.34% have been attained. The localization error, range error and transmission range performance measures are used for experimental evolution. The results recommended that proposed model is robust for navigation associated apps.

Multi-Element UWB Probe Optimization for Medical Microwave Imaging

The need for non-ionizing techniques for medical imaging applications has led to the use of microwave signals. Several systems have been introduced in recent years based on increasing the number of antennas and frequency bandwidth to obtain high resolution and good accuracy in locating objects. A novel microwave imaging system that reduces the number of required antennas for precise target location appropriate for medical applications is presented. The proposed system consists of four UWB extended gap ridge horn (EGRH) antennas covering the frequency band from 0.5 GHz to 1.5 GHz mounted on a cylindrical phantom that mimics the brain in an orthogonal set of two EGRH probes. This configuration has the ability to control both the longitudinal and transversal dimensions of the reconstructed target's image, rather than controlling the spatial resolution, by increasing the frequency band that can be easily affected by medium losses. The system is tested numerically and experimentally by the detection of a cylindrical target within a human brain model.

Multi3: multi-templates siamese network with multi-peaks detection and multi-features refinement for target tracking in ultrasound image sequences

Objective. Radiation therapy requires a precise target location. However, respiratory motion increases the uncertainties of the target location. Accurate and robust tracking is significant for improving operation accuracy. Approach. In this work, we propose a tracking framework Multi3, including a multi-templates Siamese network, multi-peaks detection and multi-features refinement, for target tracking in ultrasound sequences. Specifically, we use two templates to provide the location and deformation of the target for robust tracking. Multi-peaks detection is applied to extend the set of potential target locations, and multi-features refinement is designed to select an appropriate location as the tracking result by quality assessment. Main results. The proposed Multi3 is evaluated on a public dataset, i.e. MICCAI 2015 challenge on liver ultrasound tracking (CLUST), and our clinical dataset provided by the Chinese People’s Liberation Army General Hospital. Experimental results show that Multi3 achieves accurate and robust tracking in ultrasound sequences (0.75 ± 0.62 mm and 0.51 ± 0.32 mm tracking errors in two datasets). Significance. The proposed Multi3 is the most robust method on the CLUST 2D benchmark set, exhibiting potential in clinical practice.

MineBL: A Battery-Free Localization Scheme with Binocular Camera for Coal Mine.

Accurate localization in underground coal mining is a challenging technology in coal mine safety production. This paper proposes a low-cost battery-free localization scheme based on depth images, called MineBL. The main idea is to utilize the battery-free low-cost reflective balls as position nodes and realize underground target localization with a series of algorithms. In particular, the paper designs a data enhancement strategy based on small-target reorganization to increase the identification accuracy of tiny position nodes. Moreover, a novel ranging algorithm based on multi-filter cooperative denoising has been proposed, and an optimized weighted centroid location algorithm based on multilateral location errors has been designed to minimize underground localization errors. Many experiments in the indoor laboratories and the underground coal mine laboratories have been conducted, and the experimental results have verified that MineBL has good localization performances, with localization errors less than 30 cm in 95% of cases. Therefore, MineBL has great potential to provide a low-cost and effective solution for precise target localization in complex underground environments.

Ray‐tracing assisted fingerprinting for localization in IoT Health 4.0

AbstractThe Internet of Things (IoT) can help healthcare companies establish Ambient Assisted Living (AAL). It permits innovative solutions to a well‐known problem, notably the real‐time localization and monitoring of patients and clinicians. Yet, there is no method for indoor localization in the healthcare context that delivers centimeter‐level precision. Although radio‐based localization has intriguing potential, the primary limitations of such a method are non‐line‐of‐sight radio signal transmission and multipath reflection, both of which lead to the lowest accuracy. The advanced 5G communication technology allows for the simultaneous utilization of temporal and angular features of a broadcast signal, as well as its multipath property, to accomplish precise target localization. This article presents a method for multipath channel‐assisted localization in the AAL system by utilizing the temporal and angular measurement of the signal concurrently. We simulated and tested the localization approach in an AAL system and achieved centimeter‐level accuracy over 90% of the time in all four cases.

Episodic memory processes modulate how schema knowledge is used in spatial memory decisions

Schema knowledge can dramatically affect how we encode and retrieve memories. Current models propose that schema information is combined with episodic memory at retrieval to influence memory decisions, but it is not known how the strength or type of episodic memory (i.e., unconscious memory versus familiarity versus recollection) influences the extent to which schema information is incorporated into memory decisions. To address this question, we had participants search for target objects in semantically expected (i.e., congruent) locations or in unusual (i.e., incongruent) locations within scenes. In a subsequent test, participants indicated where in each scene the target had been located previously, then provided confidence-based recognition memory judgments that indexed recollection, familiarity strength, and unconscious memory for the scenes. In both an initial online study (n = 133) and replication (n = 59), target location recall was more accurate for targets that had been located in schema-congruent rather than incongruent locations; importantly, this effect was strongest for new scenes, decreased with unconscious memory, decreased further with familiarity strength, and was eliminated entirely for recollected scenes. Moreover, when participants recollected an incongruent scene but did not correctly remember the target location, they were still biased away from congruent regions—suggesting that detrimental schema bias was suppressed in the presence of recollection even when precise target location information was not remembered. The results indicate that episodic memory modulates how schemas are used: Schema knowledge contributes to spatial memory judgments primarily when episodic memory fails to provide precise information, and recollection can override schema bias completely.

MRI Distortion Correction and Robot-to-MRI Scanner Registration for an MRI-Guided Robotic System.

Magnetic resonance imaging (MRI) guided robotic procedures require safe robotic instrument navigation and precise target localization. This depends on reliable tracking of the instrument from MR images, which requires accurate registration of the robot to the scanner. A novel differential image based robot-to-MRI scanner registration approach is proposed that utilizes a set of active fiducial coils, where background subtraction method is employed for coil detection. In order to use the presented preoperative registration approach jointly with the real-time high speed MRI image acquisition and reconstruction methods in real-time interventional procedures, the effects of the geometric MRI distortion in robot to scanner registration is analyzed using a custom distortion mapping algorithm. The proposed approach is validated by a set of target coils placed within the workspace, employing multi-planar capabilities of the scanner. Registration and validation errors are respectively 2.05 mm and 2.63 mm after the distortion correction showing an improvement of respectively 1.08 mm and 0.14 mm compared to the results without distortion correction.