Target Detection Research Articles

Biomimetic nanozyme-based electrochemical sensor integrated with microfluidic cell for on-site uric acid detection

Uric acid (UA) is the key indicator highly associated with gout, and on-site UA detection is of great significance for personalized health management. Herein, we propose an integrated microfluidic electrochemical sensing platform for real-time UA detection. The platform mainly consists of a biomimetic nanozyme-based electrochemical sensor and a microfluidic cell. The biomimetic nanozyme with desirable catalytic activity can effectively promote UA oxidation, and the corresponding working mechanism is clarified through density functional theory calculation. Besides, the well-designed microfluidic cell is capable of impurity removal and sample storage. Based on these designs, the whole process of sample pretreatment and target detection can all be achieved on one integrated platform, allowing for user-friendly test. This developed platform shows satisfactory sensing performance for UA with a detection limit of 4.2 μM. In addition, it is successfully applied to UA determination in artificial saliva and serum samples, showing desirable application prospects in clinical diagnosis.

Moving Target Detection for Array Antennas in Varying Multipath Environments

Detecting moving targets remains a significant challenge in radar applications, particularly in dense multipath environments where signal complexities present formidable obstacles. However, the application of time reversal (TR) technology offers promising prospects for enhancing target detection capabilities. Utilizing this technology, we propose a detection algorithm tailored specifically for a moving target, designed exclusively for array antennas (AA) operating in dense multipath environments with dynamic channel variations. We establish an AA-TR signal model within this framework and derive the AA-TR likelihood ratio test (AA-TR-LRT) detector. To assess the effectiveness of our proposed approach, we also develop the AA conventional LRT (AA-C-LRT) detector as a comparative benchmark. Comprehensive numerical tests consistently verify the outstanding effectiveness of our detection algorithm, underscoring its precision in detecting moving targets, particularly in highly complex multipath scenarios with varying channels.

Target detection and simultaneous degradation of furazolidone by molecularly imprinted CoWO4/g-C3N4

In this study, a "Two birds with one stone" strategy was employed to concurrently address the challenges of electrochemical detection and photocatalytic degradation of the environmentally hazardous furazolidone (FRZ). An innovative molecularly imprinted CoWO4/g-C3N4 (i.e. MIP-CoWO4/g-C3N4) nanomaterials was constructed through a combination of hydrothermal and electrochemical polymerization techniques. The resulting MIP-CoWO4/g-C3N4 nanocomposites can be served as an exceptional sensing platform for FRZ detection, demonstrating low detection limit (2.61×10−13 mol L−1), considerable sensitivity (2.03 μA. nM−1. mm−2), excellent selectivity and decent stability. Meanwhile, the MIP-CoWO4/g-C3N4 nanocomposites exhibited remarkable photocatalytic efficiency for FRZ degradation (79.87 %). In addition, the systematic investigation into the impact of operational reaction parameters on both the electrocatalytic and photocatalytic reactions was conducted, elucidating the plausible mechanisms for both detecting and degrading FRZ. This research provides firm foundation for the development of multifunctional nanomaterials, further advancing the "integration of diagnosis and treatment" in environmental management and control.

Small aircraft detection in infrared aerial imagery based on deep neural network

Detection of aerial target is an important part of infrared image processing. Both neural network method and traditional method can be used in infrared object detection. Neural network method has many advantages such as high accuracy and good portability compared with traditional object detection method. Since the features extracted by neural network method can change over detection target, automatic feature extraction makes neural network based detection method more effective. In recent years deep learning method has been also found wide use for object detection in images. In this paper, an object detection model based on the deep learning network YOLO is constructed for solving the infrared aircraft detection problem. We construct the dataset used for training and testing with recognized features being iteratively learned. The task of infrared otject detection is sensitive to model size and detection speed. There is a requirement of using quantization method to reduce the storage space and the computation complexity. We propose a quantized model with appropriate accuracy for infrared object detection task. To solve the detection task for multiple extremely small aircrafts, model adjustment and quantization are used in proposed model and it gets a better performance. Experimental results on the constructed dataset show that the storage space for weight after quantization shrinks to a quarter, and there is no precision loss for extremely small aircrafts compared to the original model. The optimized YOLO-based deep learning model is effective to detect the small aircraft target in infrared aerial imagery.

PV-YOLO: A lightweight pedestrian and vehicle detection model based on improved YOLOv8

With the frequent occurrence of urban traffic accidents, fast and accurate detection of pedestrian and vehicle targets has become one of the key technologies for intelligent assisted driving systems. To meet the efficiency and lightweight requirements of smart devices, this paper proposes a lightweight pedestrian and vehicle detection model based on the YOLOv8n model, named PV-YOLO. In the proposed model, receptive-field attention convolution (RFAConv) serves as the backbone network because of its target feature extraction ability, and the neck utilizes the bidirectional feature pyramid network (BiFPN) instead of the original path aggregation network (PANet) to simplify the feature fusion process. Moreover, a lightweight detection head is introduced to reduce the computational burden and improve the overall detection accuracy. In addition, a small target detection layer is designed to improve the accuracy for small distant targets. Finally, to reduce the computational burden further, the lightweight C2f module is utilized to compress the model. The experimental results on the BDD100K and KITTI datasets demonstrate that the proposed PV-YOLO can achieve higher detection accuracy than YOLOv8n and other baseline methods with less model complexity.

An ultrasensitive fluorescent strategy for 17β-estradiol based on aptamer and isothermal exponential amplification reaction

The abuse of 17β-estradiol (E2) in breeding and the excretion into the environment by human beings result in exposure of E2 to humans. Excessive levels of E2 can cause an adverse effect on the human endocrine systems. Therefore, an efficient and sensitive method for the analysis of E2 is required. Herein, based on the specific capture of aptamer and the digestion od Exonuclease III, efficient amplification of isothermal exponential amplification reaction (EXPAR), a highly sensitive and selective strategy is established for E2 analysis. E2 binds to the aptamer due to their high affinity, preventing the complementary DNA (cDNA) of the aptamer from hybridizing with it. Thus, the cDNA remains unbound and freely available in the solution. Then, Exonuclease III digests the cDNA-aptamer complex, leaving the free cDNA intact. cDNA serves as primers to trigger EXPAR with the help of Bst 2.0 WarmStart DNA polymerase and Nt.BstNBⅠ nicking enzyme, achieving sensitive detection of E2. Without E2, cDNA hybridizes with the aptamer to form double-stranded DNA, which is degraded by Exonuclease III. As a result, the amplification reaction fails to initiate. The approach shows a linear range of 100 aM-10 pM, spanning 5 orders of magnitude, with the limit of detection as low as 36 aM. In addition, the selectivity and reproducibility are great, and the recoveries in tap water and milk ranged from 99.4% to 117.3%, suggesting good practicability. This method may also provide a new possibility for highly sensitive detection of other small-molecule targets via using the corresponding aptamers.

A chip-based universal strategy to realize multiplex PCR by using wax films for sealing and controllable release of primers

Simultaneous detection of multiple nucleic acid targets from a single sample is a common requirement in molecular diagnosis and basic research. Dividing a bulky polymerase chain reaction (PCR) into many isolated small reaction units through microfluidic technology is commonly used to realize this goal. However, previous microfluidic platforms for multiplex PCR suffer from complex structures and strict operation requirements. In this study, a chip-based universal strategy is constructed to realize multiplex PCR by using wax films for sealing and controllable release of prespotted primers. This microfluidic chip contains twenty-four reaction chambers connected in series by a single channel, and the bottom of each reaction chamber is covered by a wax film generated by solvent volatilization. These wax films can prevent the prespotted primers from being flushed away by the reaction mixture during the injection process. After thermal blocking of the connecting channel to isolate each reaction chamber, the chip is placed on a flat thermal cycler. The wax films melt during the denaturing step of PCR so that the primers are released and mixed with the reaction mixture, a process seamlessly compatible with PCR thermal cycling. After fluid flow simulation and carefully examining its basic performance, this chip was applied to genotype seven deafness-associated hotspot mutations using a competitive allele-specific PCR assay. The genotyping results of clinical samples using this chip were totally concordant with those obtained by Sanger sequencing, demonstrating the practical utility of this universal strategy for multiplex PCR detection.

Highly Responsive Cryogel Based Sensing Platform by Encapsulating Programmed DNA for Colorimetric Detection of 17β-estradiol

Responsive hydrogels show obvious superiorities when being employed at detection owing to their polymeric networks and optical properties. Albeit important, the slow response of traditional hydrogels against targets limits their further applications, and enhancing the responsiveness of hydrogel is therefore imperative. Herein, a cryogel with hierarchical structures was designed through adjusting the hydrogel structure by the freezing process to improve the responsive properties, and fabricated for rapid analysis of 17β-estradiol (E2). The cryogel encapsulating the DNA hairpins rapidly adsorbed the complementary DNA isolated from the aptamer in the presence of E2 and activated an efficient hybridization chain reaction (HCR). After integration with a smartphone system, a portable colorimetric sensing platform was created for sensitive detection of E2 with a low detection limit of 0.5 nM. Our work provides a promising way for highly responsive hydrogel-based biosensor design, and broadens their applications in rapid and portable target detection.

Character region extraction of wheel water meter based on object detection

Currently, research on automatic meter reading mainly focuses on meter reading recognition, while neglecting the fundamental role of counter detection in the entire automatic meter reading system. In fact, only by accurately locating the counter area can the influence of dial factors be completely eliminated, thus ensuring the accuracy and reliability of subsequent water meter reading recognition. In view of this phenomenon, the focus of this study is on the counter detection stage. Firstly, a target detection-based image skew correction method is proposed to solve the problem of image skew caused by shooting angle and other reasons. This method ensures the accuracy of subsequent counter area positioning and the neatness of cutting effect. Secondly, a semi-supervised target detection training method is proposed to solve the problem of time and manpower costs required in large-scale data situations. In addition, we have made publicly available a dataset containing 1070 water meter images for non-commercial purposes, which can be obtained from the Github11https://github.com/QuanhuiZhao/water-datasets.. Finally, we evaluated our model on three completely different datasets and compared it with the best positioning results of other models. The experimental results show that compared with other models, the proposed model in this paper has improved the positioning accuracy by 5.82%, 5.96%, and 9.20% on three datasets respectively. Furthermore, in the final visualization comparison, the model accurately identifies the counter region even when faced with complex real-world environments.

HTD-TS3: Weakly Supervised Hyperspectral Target Detection Based on Transformer via Spectral-Spatial Similarity.

As an advanced technique in remote sensing, hyperspectral target detection (HTD) is widely concerned in civilian and military applications. However, the limitation of prior and heterogeneous backgrounds makes HTD models sensitive to data corruption under various interference from the environment. In this article, a novel united HTD framework based on the concept of transformer is proposed to extract [HTD based on transformer via spectral-spatial similarity (HTD-TS3)] under weak supervision, which opens up more flexible ways to study HTD. For the first time, the transformer mechanism is introduced into the HTD task to extract spectral and spatial features in a unified optimization procedure. By modeling long-range dependence among spectra, it realizes spectral-spatial joint inference based on long-range context, which addresses the issues of insufficient utilization of spatial information. To provide samples for weakly supervised learning (WSL), the coarse sample selection and spectral sequence construction in an efficient way are proposed, which makes full use of limited prior information. Finally, an exponential constrained nonlinear function is adopted to acquire pixel-level prediction via combining discriminative spectral-spatial features and coarse spatial information. Experiments on real hyperspectral images (HSIs) captured by different sensors at various scenes verify the effectiveness and efficiency of HTD-TS3.

Intelligent optimization of horizontal wellbore trajectory based on reinforcement learning

Ultra-long horizontal wells are crucial for improving the production and developmental benefits of shale oil and gas wells. Currently, advanced downhole semi-closed-loop steering drilling technology depends on a dual communication mechanism between the surface and downhole, as well as the empirical decisions of human experts. However, it is difficult to acutely control the trajectory of ultra-long horizontal open hole drilling in a reservoir owing to geological uncertainties related to shale oil and gas, the uncertainty of the drilling tool's building capacity, and the lag of measurement information while drilling. This report proposes an adaptive target detection and design method for ultra-long horizontal well trajectories based on reinforcement learning. The developed approach enables dynamic identification of reservoir sequences according to logging-while-drilling data and prediction of horizontal targets in real-time, with a recognition accuracy of 90.1%. Additionally, measurement-while-drilling data are used to accurately characterize the depth, inclination, and azimuth of the target-entering process. The dynamic interaction mechanism between the bit and the target environment is simulated by defining the bit action, reward function, and an update mechanism. The drilling engineering conditions restrict the interactions, such that the bit automatically decides the direction in which to drill the targets, demonstrating 100% accuracy for the wellbore trajectory toward the target. The experimental application results indicate that the developed method can be applied to determine the dynamic target area of a horizontal well in real time and make intelligent downhole decisions regarding ultra-long horizontal section borehole trajectory adjustments while drilling and measuring. The drilling rate of high-quality reservoirs is therefore significantly improved. The results discussed herein provide insights to support further developments of downhole full-closed-loop intelligent autonomous decision-making borehole trajectory control.

Line spectrum tracking method in transformed signal space for underwater moving targets in low signal-to-noise ratio environment

Line spectrum tracking is an essential signal-processing method for underwater passive detection. However, its performance is often seriously degraded due to signal fluctuation, especially in low signal-to-noise ratio scenarios. In this paper, based on signal space transformation and hidden Markov model, a signal trajectory tracking method is proposed for underwater moving target detection and parameter estimation. With this method, tracking the varying line spectrum trajectory in three-dimensional frequency-azimuth-time signal space is constrained onto a two-dimensional plane in the transformed signal space. Not only is the computation complexity reduced, but the ability to track weak line spectrums and estimate target parameters is improved. The performance of this method is verified with numerical simulations and experimental data processing.

Machine vision-based detection of surface defects in cylindrical battery cases

Automotive 21700 series lithium batteries are prone to surface defects during production and transportation, thus affecting their performance, so we propose a full-surface defect detection method for battery cases based on the synthesis of traditional image processing and deep learning to address this problem. First, the mechanism of surface defects on a battery case is analysed, and the types of surface defects are summarized. A suitable platform for image acquisition is designed for the severely reflective surface. Since there is no publicly available defect dataset for cylindrical battery cases, a defect dataset is established, and the dataset is augmented and expanded via the traditional method and the ACGAN model. For the defects on the top of the battery case, a traditional image processing algorithm is used to combine the roundness and the area of the area pixels to make a comprehensive judgement. For the defects on the bottom and side of the battery case, after comparing and analysing a variety of deep learning networks, the YOLOv7 model is selected to address the data characteristics of the large experimental inputs and the small targeted defects. To improve the accuracy of the model to meet the needs of real-time detection in industry, the CA attention mechanism, the DYHEAD dynamic detector head, and the slicing-assisted super inference (SAHI) method are used, with a final map accuracy reaching 98.1 %, which is 2.7 % higher than that of the initial model. Compared with mainstream target detection algorithms, the algorithm in this paper has good detection performance for cylindrical battery case defect detection and can be better applied to real-time detection in industry.

Deghosting method for cross localization in distributed high frequency surface wave radar

AbstractIn this article, the challenging problem of multi‐target cross localization within a novel distributed high‐frequency surface wave radar (HFSWR) is tackled. This system consists of two transmitter‐receiver pairs, each of which can correspondingly generate a set of Range‐Doppler measurements. By associating the Range‐Doppler measurements from the same target, the individual target location can be derived using the associated measurements. However, when multiple targets are involved, it is unknown which measurements originate from the same target, leading to ambiguities in the measurement‐to‐measurement association. False associations can result in ghosts, ultimately degrading the overall system performance. To deal with the ghosts, a three‐stage deghosting method is proposed that incorporates the geometric characteristics of the system, the velocity features of the targets, and the motion features of the targets across multiple frames. The simulation results demonstrate the effectiveness of the proposed method compared with the other investigated deghosting algorithms. The proposed approach exhibits robust target detection capability while significantly reducing both the OSPA error and the cardinality error.

An intelligence enhancement method for USV navigation visual measurement based on variable gradient soft-threshold correction

Maritime image enhancement is crucial in marine engineering for applications like unmanned surface vessels (USV) visual measurement and path planning. This paper proposes an image enhancement algorithm based on variable gradient soft-threshold correction (VGSC), tailored for USV intelligent navigation. By calculating the gradient of the two-dimensional difference quotient function of the pixels, we determine the pixel distribution and threshold. An innovative dynamic equalization function is applied to compress maximum pixel values and stretch minimum ones, allowing for adaptive adjustment of the threshold. The results indicate that the VGSC algorithm effectively suppresses background noise, thereby facilitating the extraction of target contour pixel information. When evaluated using four image assessment indicators, the VGSC algorithm enhances the image quality by 20.09 % and 28.92 % compared to bilateral filtering and Gamma correction, respectively. This method excels in enhancing image details and suppressing noise, promising robust application in multi-modal data fusion and precise maritime target detection.

A growth posture identification method of immature peaches in natural environments

The precise bagging of immature peaches by the fruit bagging robot requires the identification of the target young fruits as well as the acquisition of their growth angles. The network models employed in single detection algorithms exhibit complexity and pose challenges for deployment on mobile terminals as they heavily rely on the availability of labeled samples. A semi-supervised learning and lightweight strategy based on YOLOv8n-seg was proposed to identify the growth posture of immature peaches in the work. Firstly, the self-training method and data enhancement in semi-supervised learning were used to generate efficient pseudo-label data. A tremendous labeling workload was solved. Secondly, the YOLOv8n-seg backbone network was replaced with an improved MobileNetv3 structure for better real-time detection on MT to reduce network parameters and calculations. The model was easily deployed with improved detection speed. Meanwhile, the original upsampling module was replaced with the CARAFE module to enhance the recognition capability of global features, which considered the impact of immature peaches on the nearby color background. The CIOU loss function was ultimately substituted with the SIOU loss function to further optimize the boundary frame loss and target detection accuracy. The enhanced model could predict the coordinate information of immature peaches and calculate growth angles. The experimental results show that the improved peach seedling growth posture network model has a weight size of only 23.1% of the original model. Additionally, the algorithm achieved a remarkable rate of 87.8% in identifying young peach fruits, with an average error of ±3.3° in estimating the growth angle. It took an average of 31 ms to detect a 3024 × 4032 pixels image in the edge computing device JETSON AGX ORIN CLB development kits. The method could rapidly identify immature peaches and estimate the growth angle, which ensured accurate bagging.

Intelligent Maritime Radar Target Detection With Partial Annotation via Progressive Learning

Intelligent Maritime Radar Target Detection With Partial Annotation via Progressive Learning

DCS-YOLOv5s: A Lightweight Algorithm for Multi-Target Recognition of Potato Seed Potatoes Based on YOLOv5s

The quality inspection of potato seed tubers is pivotal for their effective segregation and a critical step in the cultivation process of potatoes. Given the dearth of research on intelligent tuber-cutting machinery in China, particularly concerning the identification of bud eyes and defect detection, this study has developed a multi-target recognition approach for potato seed tubers utilizing deep learning techniques. By refining the YOLOv5s algorithm, a novel, lightweight model termed DCS-YOLOv5s has been introduced for the simultaneous identification of tuber buds and defects. This study initiates with data augmentation of the seed tuber images obtained via the image acquisition system, employing strategies such as translation, noise injection, luminance modulation, cropping, mirroring, and the Cutout technique to amplify the dataset and fortify the model’s resilience. Subsequently, the original YOLOv5s model undergoes a series of enhancements, including the substitution of the conventional convolutional modules in the backbone network with the depth-wise separable convolution DP_Conv module to curtail the model’s parameter count and computational load; the replacement of the original C3 module’s Bottleneck with the GhostBottleneck to render the model more compact; and the integration of the SimAM attention mechanism module to augment the model’s proficiency in capturing features of potato tuber buds and defects, culminating in the DCS-YOLOv5s lightweight model. The research findings indicate that the DCS-YOLOv5s model outperforms the YOLOv5s model in detection precision and velocity, exhibiting superior detection efficacy and model compactness. The model’s detection metrics, including Precision, Recall, and mean Average Precision at Intersection over Union thresholds of 0.5 (mAP1) and 0.75 (mAP2), have improved to 95.8%, 93.2%, 97.1%, and 66.2%, respectively, signifying increments of 4.2%, 5.7%, 5.4%, and 9.8%. The detection velocity has also been augmented by 12.07%, achieving a rate of 65 FPS. The DCS-YOLOv5s target detection model, by attaining model compactness, has substantially heightened the detection precision, presenting a beneficial reference for dynamic sample target detection in the context of potato-cutting machinery.

Single gold nanowire-nanoparticles conjugated system: Fabrication and sensing application

Single-entity-based sensing platform has many advantages for real-time assays, such as single-molecule analysis, or targets detection in confined environment. In this contribution, a new single Au nanowire (NW) – Au@Pt/Au nanoparticles (Au@Pt/Au NPs) conjugated system was established and used for the detection of thrombin by using surface-enhanced Raman spectroscopy (SERS) technique. This method was mainly based on electrostatic attraction between the capture (thrombin aptamer) and probe molecules (crystal violet, CV) on the surface of Au NW - Au@Pt/Au NPs conjugation, reducing the adsorption of CV molecules on conjugation surface, and resulting the decrease of SERS signals. The addition of thrombin could bind with thrombin aptamer due to specific interaction, leading to the decrease of electrostatic effect from thrombin aptamer for CV diffusion to conjugation surface, and the SERS signals could be recovered. This thrombin sensing method showed high sensitivity and selectivity, and the linear range for thrombin assay was 0.01 nM–100 nM with a detection limit of 1.35 pM. This single Au NW - Au@Pt/Au NPs conjugation has more SERS hotspots and small overall size, which offers unparalleled advantages over other sensing system and can be applicable for real time analysis, especially at single molecule/cell level.

SGW-YOLOv8n: An Improved YOLOv8n-Based Model for Apple Detection and Segmentation in Complex Orchard Environments

This study addresses the problem of detecting occluded apples in complex unstructured environments in orchards and proposes an apple detection and segmentation model based on improved YOLOv8n-SGW-YOLOv8n. The model improves apple detection and segmentation by combining the SPD-Conv convolution module, the GAM global attention mechanism, and the Wise-IoU loss function, which enhances the accuracy and robustness. The SPD-Conv module preserves fine-grained features in the image by converting spatial information into channel information, which is particularly suitable for small target detection. The GAM global attention mechanism enhances the recognition of occluded targets by strengthening the feature representation of channel and spatial dimensions. The Wise-IoU loss function further optimises the regression accuracy of the target frame. Finally, the pre-prepared dataset is used for model training and validation. The results show that the SGW-YOLOv8n model significantly improves relative to the original YOLOv8n in target detection and instance segmentation tasks, especially in occlusion scenes. The model improves the detection mAP to 75.9% and the segmentation mAP to 75.7% and maintains a processing speed of 44.37 FPS, which can meet the real-time requirements, providing effective technical support for the detection and segmentation of fruits in complex unstructured environments for fruit harvesting robots.