Target Detection Research Articles

Surveillance for Chlamydia trachomatis variants escaping detection with the Aptima Combo 2 assay in Canada from 2019 to 2021.

Molecular tests are commonly used for the detection of sexually transmitted infections (STIs) like Chlamydia trachomatis. Mutations impacting C. trachomatis molecular target detection on the Hologic Panther AC2 assay have been reported in several countries, raising concerns about potential false negative results. This study showed C. trachomatis target detection failures in specimens submitted for C. trachomatis testing in Canadian laboratories from 2019 to 2021. A reformulated version of the AC2 molecular test is now available that can identify C. trachomatis strains harboring target site mutations that were impacted by the previous test formulation. While target site mutations were rare in Canada, revealing their presence is important to ensure accurate molecular detection of C. trachomatis with existing testing methods. This study supports ongoing genetic monitoring of C. trachomatis molecular test target sites, as well as the use of the reformulated test to avoid false negative results and subsequent transmissions.

Automatic visual detection of activated sludge microorganisms based on microscopic phase contrast image optimisation and deep learning.

The types and quantities of microorganisms in activated sludge are directly related to the stability and efficiency of sewage treatment systems. This paper proposes a sludge microorganism detection method based on microscopic phase contrast image optimisation and deep learning. Firstly, a dataset containing eight types of microorganisms is constructed, and an augmentation strategy based on single and multisamples processing is designed to address the issues of sample deficiency and uneven distribution. Secondly, a phase contrast image quality optimisation algorithm based on fused variance is proposed, which can effectively improve the standard deviation, entropy, and detection performance. Thirdly, a lightweight YOLOv8n-SimAM model is designed, which introduces a SimAM attention module to suppress the complex background interference and enhance attentions to the target objects. The lightweight of the network is realised using a detection head based on multiscale information fusion convolutional module. In addition, a new loss function IW-IoU is proposed to improve the generalisation ability and overall performance. Comparative and ablative experiments are conducted, demonstrating the great application potential for rapid and accurate detection of microbial targets. Compared to the baseline model, the proposed method improves the detection accuracy by 12.35% and hastens the running speed by 37.9 frames per second while evidently reducing the model size.

A generative adversarial network with multiscale and attention mechanisms for underwater image enhancement

Underwater images collected are often of low clarity and suffer from severe color distortion due to the marine environment and Illumination conditions. This directly impacts tasks such as marine ecological monitoring and underwater target detection, which rely on image processing. Therefore, enhancing Underwater images to improve their quality is necessary. A generative adversarial network with an encoder-decoder structure is proposed to improve the quality of Underwater images. The network consists of a generative network and an adversarial network. The generative network is responsible for enhancing the images, while the adversarial network determines whether the input is an enhanced image or a real high-quality image. In the generative network, we first design a residual convolution module to extract more texture and edge information from underwater images. Next, we design a multi-scale dilated convolution module to capture underwater features at different scales. Then, we design a feature fusion adaptive attention module to reduce the interference of redundant features and enhance the local perception capabilities. Finally, we construct the generative network using these modules along with conventional modules. In the adversarial network, we first design a multi-scale feature extraction module to improve the feature extraction ability. We then use the multi-scale feature extraction module along with conventional convolution modules to design the adversarial network. Additionally, we propose an improved loss function by introducing color loss into the conventional loss function. The improved loss function can better measure the color discrepancy between the enhanced image and the real image. It is useful to reduce color distortion in the enhanced images. In experimental simulations, the images enhanced by the proposed methods have the highest PSNR, SSIM, and UIQM values, indicating that the proposed method has superior Underwater image enhancement capabilities compared to other methods.

Enhanced YOLOv8-based method for space debris detection using cross-scale feature fusion

Optical observations play a crucial role in monitoring space debris, and long exposure large field-of-view telescopes exhibit robust detection capabilities for identifying space debris. Nevertheless, a substantial volume of data, intricate noise, nonlinearity, and target discontinuities significantly affect the observational process. To address these intricate celestial background conditions, an enhanced YOLOv8-based method for spatial debris detection is introduced into this study. Initially, a cross-scale feature fusion module is incorporated into the neck network, followed by a subtle processing step for the feature fusion component. Finally, the content-aware reassembly of features module is employed to replace the original upsampling module, which enhances the efficiency and accuracy of feature reconstruction, thereby achieving effective detection and identification of spatial debris targets. The study utilized a dataset comprising astronomical images captured by an open-source large-field-of-view optical telescope. The experimental results show that the detection accuracy and speed of the method are improved, and that they can meet the requirements of space debris detection in complex backgrounds.

ESCL-YOLO: a target detection algorithm for complex underwater environments based on improved YOLOv8

ESCL-YOLO: a target detection algorithm for complex underwater environments based on improved YOLOv8

Traffic target detection based on context enhancement and feature purification

Traffic target detection based on context enhancement and feature purification

Innovative segmentation technique for aerial power lines via amplitude stretching transform

Accurate segmentation of power line targets helps quickly locate faults, evaluate line conditions, and provides key image data support and analysis for the safe and stable operation of the power system.The aerial power line in segmentation due to the target is small, and the imaging reflected energy is weak, so the Unmanned Aerial Vehicle (UAV) aerial power line image is very susceptible to the interference of the environment line elements and noise, resulting in the detection of the power line target in the image of the defective, intermittent, straight line interferences and other low accuracy and real-time efficiency is not high. For this reason, this paper designs a pure amplitude stretching kernel function to form a Fourier amplitude vector field and uses this amplitude vector field to implement the stretching transformation of the amplitude field of the aerial power line image, so that the angular field after the Fourier inverse transformation can better react to the spatial domain line targets, and finally, after the Relative Total Variation (RTV) processing, the power line can be well detected. The proposed algorithm is compared with the main power line segmentation algorithms, such as Region Convolutional Neural Networks(R-CNN) and Phase Stretch Transform(PST). The average values of evaluation indicators PPA, MMPA and MMIoU of the image segmentation results of the proposed algorithm reach 0.96, 0.96 and 0.95 respectively, and the average time lag of detection is less than 0.2s, indicating that the accuracy and real-time performance of the segmentation results of the proposed algorithm are significantly better than those of the above algorithms.

A multi-scale rotated ship targets detection network for remote sensing images in complex scenarios

Detecting ship targets in remote sensing images within complex scenarios faces numerous challenges. The limited feature information of small-scale targets and their random orientation angles often result in missed and false detections. To address these issues, this paper proposes a Multi-Scale Rotated Detection Network (MSRO-Net) for detecting rotated ship targets in remote sensing images. The network adopts a CNN-Transformer hybrid architecture for collaborative feature extraction and integrates our proposed Coordinate-Aware Pyramid Feature Aggregation module (CAPP). This backbone network retains the capability of local feature extraction while also connecting global context and capturing long-range dependencies. The model can gather information from any position in the sequence, extract contextual information of targets at different scales, and enhance global feature representation. Additionally, this paper proposes an Upsampling Feature Reconstruction Pyramid (ARFPN-C), based on Adaptive Rotated Convolution (ARC). This network combines ARC adaptive rotating convolution with a multi-scale feature fusion mechanism to enhance the model’s perceptual capability, addressing the issues of limited target feature information and random orientation angles. The proposed algorithm is validated on two public remote sensing image datasets, HRSC2016 and DOTA. The mAP07, mAP12 on the HRSC2016 dataset, as well as the mAP in the ship category of the DOTA dataset, show a significant advantage over other commonly used object detection algorithms, with accuracies of 90.70%, 98.98%, and 89.46%, respectively. These results further validate the accuracy and effectiveness of the proposed MSRO-Net in object detection.

Improved printed circuit board defect detection scheme

In this paper, an improved printed circuit board(PCB)defect detection scheme named PD-YOLOv8 is proposed, which is specialized in the common and challenging problem of small target recognition in PCB inspection. This improved scheme mainly relies on the basic framework of YOLOv8n, and effectively enhances the detection performance of PCB small defects through multiple innovative designs. First, we incorporate the Efficient Channel Attention Network (ECANet) attention mechanism into the backbone network of YOLOv8, which improves the performance of small-target detection by adaptively enhancing the expressiveness of key features, so that the network possesses higher sensitivity and focus on tiny details in PCB images. Second, we optimize and upgraded the neck structure. On the one hand, the module is introduced to facilitate cross-layer feature fusion to ensure that the rich texture information at the lower layer and the abstract semantic information at the higher layer complement each other, which is conducive to improving the contextual understanding of small target detection; on the other hand, a detection head specialized for small targets is designed and added to enhance the ability of locating and identifying tiny defects. Furthermore, in order to further enhance the interaction and fusion of multi-scale features, we also add a SlimNeck module to the neck structure, which realizes efficient information transfer through streamlined design and reduces computational complexity at the same time. In addition, we draw on the advanced BiFPN structure, which enables the bidirectional flow of feature information between multiple layers and greatly improves the capture and integration of small target features. Compared to the original YOLOv8 algorithm, this algorithm improves the average accuracy on small targets by for mAP50.

DBnet: A Lightweight Dual-Backbone Target Detection Model Based on Side-Scan Sonar Images

Due to the large number of parameters and high computational complexity of current target detection models, it is challenging to perform fast and accurate target detection in side-scan sonar images under the existing technical conditions, especially in environments with limited computational resources. Moreover, since the original waterfall map of side-scan sonar only consists of echo intensity information, which is usually of a large size, it is difficult to fuse it with other multi-source information, which limits the detection accuracy of models. To address these issues, we designed DBnet, a lightweight target detector featuring two lightweight backbone networks (PP-LCNet and GhostNet) and a streamlined neck structure for feature extraction and fusion. To solve the problem of unbalanced aspect ratios in sonar data waterfall maps, DBnet employs the SAHI algorithm with sliding-window slicing inference to improve small-target detection accuracy. Compared with the baseline model, DBnet has 33% fewer parameters and 31% fewer GFLOPs while maintaining accuracy. Tests performed on two datasets (SSUTD and SCTD) showed that the mAP values improved by 2.3% and 6.6%.

Smart Agricultural Pest Detection Using I-YOLOv10-SC: An Improved Object Detection Framework

Aiming at the problems of insufficient detection accuracy and high false detection rates of traditional pest detection models in the face of small targets and incomplete targets, this study proposes an improved target detection network, I-YOLOv10-SC. The network leverages Space-to-Depth Convolution to enhance its capability in detecting small insect targets. The Convolutional Block Attention Module is employed to improve feature representation and attention focus. Additionally, Shape Weights and Scale Adjustment Factors are introduced to optimize the loss function. The experimental results show that compared with the original YOLOv10, the model generated by the improved algorithm improves the accuracy by 5.88 percentage points, the recall rate by 6.67 percentage points, the balance score by 6.27 percentage points, the mAP value by 4.26 percentage points, the bounding box loss by 18.75%, the classification loss by 27.27%, and the feature point loss by 8%. The model oscillation has also been significantly improved. The enhanced I-YOLOv10-SC network effectively addresses the challenges of detecting small and incomplete insect targets in tea plantations, offering high precision and recall rates, thus providing a solid technical foundation for intelligent pest monitoring and precise prevention in smart tea gardens.

An Investigation of Infrared Small Target Detection by Using the SPT–YOLO Technique

To detect and recognize small-size and submerged complex background targets in infrared images, we combine a dynamic receptive field fusion strategy and a multi-scale feature fusion mechanism to improve the detection performance of small targets significantly. The space-to-depth convolution module is introduced as a downsampling layer in the backbone first and achieves the same sampling effect. More detailed information is retained at the same time. Thus, the model’s detection capability for small targets has been enhanced. Then, the pyramid level 2 feature map with minimum receptive field and maximum resolution is added to the neck, which reduces the loss of positional information during feature sampling. Furthermore, x-small detection heads are added, the understanding of the overall characteristics and structure of the target is enhanced much more, and the representation and localization of small targets have been improved. Finally, the cross-entropy loss function in the original network model is replaced by an adaptive threshold focal loss function, forcing the model to allocate more attention to target features. The above methods are based on a public tool, the eighth version of You Only Look Once (YOLO) improved, it is named SPT–YOLO (SPDConv + P2 + Adaptive Threshold + YOLOV8s) in this paper. Some experiments on datasets such as infrared small object detection (IR-SOD) and infrared small target detection 1K(IRSTD-1K), etc. have been executed to verify the proposed algorithm; and the mean average precision of 94.0% and 69% under the condition of threshold at 0.5 and over a range from 0.5 to 0.95 is obtained, respectively. The results show that the proposed method achieves the best performance of infrared small target detection compared to existing methods.

The Application of an Intelligent Agaricus bisporus-Harvesting Device Based on FES-YOLOv5s.

To address several challenges, including low efficiency, significant damage, and high costs, associated with the manual harvesting of Agaricus bisporus, in this study, a machine vision-based intelligent harvesting device was designed according to its agronomic characteristics and morphological features. This device mainly comprised a frame, camera, truss-type robotic arm, flexible manipulator, and control system. The FES-YOLOv5s deep learning target detection model was used to accurately identify and locate Agaricus bisporus. The harvesting control system, using a Jetson Orin Nano as the main controller, adopted an S-curve acceleration and deceleration motor control algorithm. This algorithm controlled the robotic arm and the flexible manipulator to harvest Agaricus bisporus based on the identification and positioning results. To confirm the impact of vibration on the harvesting process, a stepper motor drive test was conducted using both trapezoidal and S-curve acceleration and deceleration motor control algorithms. The test results showed that the S-curve acceleration and deceleration motor control algorithm exhibited excellent performance in vibration reduction and repeat positioning accuracy. The recognition efficiency and harvesting effectiveness of the intelligent harvesting device were tested using recognition accuracy, harvesting success rate, and damage rate as evaluation metrics. The results showed that the Agaricus bisporus recognition algorithm achieved an average recognition accuracy of 96.72%, with an average missed detection rate of 2.13% and a false detection rate of 1.72%. The harvesting success rate of the intelligent harvesting device was 94.95%, with an average damage rate of 2.67% and an average harvesting yield rate of 87.38%. These results meet the requirements for the intelligent harvesting of Agaricus bisporus and provide insight into the development of intelligent harvesting robots in the industrial production of Agaricus bisporus.

Multi-Type Ship Target Detection in Complex Marine Background Based on YOLOv11

Realizing accurate control of ship target information in complex marine environments is of great significance for maintaining marine environment security and safeguarding maritime sovereignty. With the rapid development of material technology and manufacturing industry, the types and styles of ships are increasing, and the distribution of multi-type ships on the sea is widespread. How to realize the accurate detection and identification of dynamic multi-type ship targets in the complex marine environment is an important and difficult problem that needs to be solved urgently in current marine environment detection. In this paper, an improved YOLOv11 ship target detection algorithm is proposed, which firstly utilizes the improved EfficientNetv2 network to replace the original backbone network of YOLOv11 to improve the learning ability of ship features under complex sea conditions; in order to solve the problem of interference by moving objects at sea when detecting dense ship targets and reduce the problems of missing detection and false alarms, the algorithm borrows from ConvNext block idea in the process of a neck feature pyramid network fusion; the algorithm introduces the WIoU loss function, which compensates for the effect of the small number of pixels of the small target in the process of regression loss computation, so as to improve the network’s performance in detecting small targets. In order to test the network performance in actual application scenarios, the article builds a visible ship target dataset, including complex background, occlusion and overlap, small targets, and other factors. Through experimental verification, the detection accuracy of the improved algorithm is improved by 5.6% compared with the original algorithm, and compared with typical algorithms in terms of detection accuracy, speed, and number of parameters, ablation experiments are designed to comprehensively validate and analyze the algorithm’s performance.

Infrared Dim Small Target Detection Algorithm with Large-Size Receptive Fields

Infrared Dim Small Target Detection Algorithm with Large-Size Receptive Fields

Integrated Sensing and Communication Target Detection Framework and Waveform Design Method Based on Information Theory.

Target detection is a core function of integrated sensing and communication (ISAC) systems. The traditional likelihood ratio test (LRT) target detection algorithm performs inadequately under low signal-to-noise ratio (SNR) conditions, and the performance of mainstream orthogonal frequency division multiplexing (OFDM) waveforms declines sharply in high-speed scenarios. To address these issues, an information-theory-based orthogonal time frequency space (OTFS)-ISAC target detection processing framework is proposed. This framework adopts the OTFS waveform as its fundamental signal. The target detection is implemented through a relative entropy test (RET) comparing echo signals against target presence/absence hypotheses. Furthermore, to enhance the system's target detection capability, the iterative OTFS-ISAC waveform design (I-OTFS-WD) method which maximizes the relative entropy is proposed. This method utilizes the minorization-maximization (MM) algorithm framework and semidefinite relaxation (SDR) technique to transform the non-convex optimization problem into an iterative convex optimization problem for resolution. The simulation results demonstrate that, under sufficient sample conditions, the RET algorithm achieves a 9.12-fold performance improvement over LRT in low-SNR scenarios; additionally, the optimized waveform reduces the sample requirements of the RET algorithm by 40%, further enhancing the target detection capability of the OTFS-ISAC system.

Advanced Scattering Analysis of Targets Under Plane Waves and Electromagnetic Vortex Waves for Stealth Applications

This study investigates the scattering characteristics of typical metallic targets under the influence of electromagnetic vortex waves, with a focus on Radar Cross Section (RCS) and Orbital Angular Momentum Radar Cross Section (ORCS). Using the physical optics approximation, the induced current density on the target surface was calculated, and the backscattered field was derived. The comparative analysis highlights the advantages of electromagnetic vortex waves over plane waves in stealth applications, with an in-depth examination of the impact of different OAM modes on power distribution and flat plate scattering characteristics. The unique properties of electromagnetic vortex waves show promising applications in stealth radar and target detection. Furthermore, the study discusses the potential for using OAM waves in enhancing radar system performance, target identification, and electronic countermeasures. The ability of OAM waves to provide selective detection capabilities and reduce RCS under certain conditions makes them particularly valuable for military stealth applications. The combination of MIMO technology with OAM waves is also explored as a means to further improve detection capabilities and flexibility. The findings suggest that electromagnetic vortex waves have a broad range of applications beyond stealth, including radar sensing, imaging, and rotational Doppler detection, thereby opening new avenues for advanced radar technologies

Fast‐Tuning and Narrow‐Linewidth Hybrid Laser for FMCW Ranging

AbstractPossessing a long coherent length, high repetition rate, and fast frequency‐sweeping laser sources with narrow linewidth is crucial components in coherent frequency‐modulated continuous‐wave (FMCW) light detection and ranging (LiDAR) systems. While these attributes are realized individually in standalone devices, the integration of these features into a single laser represents a significant advancement in the field. In this study, a hybrid integrated laser that achieves a linewidth of 9 kHz, a wide frequency‐modulation response extending up to 68 MHz, and a low chirp nonlinearity of 4.3 × 10−6 at a repetition rate of 100 kHz is presented. The achievement of this performance is made possible through self‐injection locking of a DFB laser diode to a low‐loss Si3N4 micro‐ring resonator on the dual‐layer Si3N4‐Si platform. Through the application of a fast‐converging pre‐distortion algorithm and a driving signal with 150 mV amplitude, a linear FMCW signal with 1.05 GHz frequency excursion is generated. Exploiting the wideband FM response of the PIN phase shifter, a frequency‐agile FMCW light source engine capable of generating linear FMCW signals at repetition rates of up to 2 MHz is successfully developed. Leveraging these cutting‐edge capabilities, an FMCW LiDAR ranging system for target detection across varying distances, achieving a high ranging precision of 0.4 cm for targets at 6.2 m, is implemented. This innovative work not only demonstrates the feasibility of integrating multiple advanced functionalities into a single laser but also demonstrates the potential for enhancing the resolution and precision of FMCW LiDAR systems for a wide range of applications.

SVDDD: SAR Vehicle Target Detection Dataset Augmentation Based on Diffusion Model

In the field of target detection using synthetic aperture radar (SAR) images, deep learning-based supervised learning methods have demonstrated outstanding performance. However, the effectiveness of deep learning methods is largely influenced by the quantity and diversity of samples in the dataset. Unfortunately, due to various constraints, the availability of labeled image data for training SAR vehicle detection networks is quite limited. This scarcity of data has become one of the main obstacles hindering the further development of SAR vehicle detection. In response to this issue, this paper collects SAR images of the Ka, Ku, and X bands to construct a labeled dataset for training Stable Diffusion and then propose a framework for data augmentation for SAR vehicle detection based on the Diffusion model, which consists of a fine-tuned Stable Diffusion model, a ControlNet, and a series of methods for processing and filtering images based on image clarity, histogram, and an influence function to enhance the diversity of the original dataset, thereby improving the performance of deep learning detection models. In the experiment, the samples we generated and screened achieved an average improvement of 2.32%, with a maximum of 6.6% in mAP75 on five different strong baseline detectors.

Programmable DNA Nanoswitch-Regulated Plasmonic CRISPR/Cas12a-Gold Nanostars Reporter Platform for Nucleic Acid and Non-Nucleic Acid Biomarker Analysis Assisted by a Spatial Confinement Effect.

CRISPR/Cas 12a system based nucleic acid and non-nucleic acid targets detection faces two challenges including (1) multiple crRNAs are needed for multiple biomarkers detection and (2) insufficient sensitivity resulted from photobleaching of fluorescent dyes and the low kinetic cleavage rate for a traditional single-strand (ssDNA) reporter. To address these limitations, we developed a programmable DNA nanoswitch (NS)-regulated plasmonic CRISPR/Cas12a-gold nanostars (Au NSTs) reporter platform for detection of nucleic acid and non-nucleic acid biomarkers with the assistance of the spatial confinement effect. Through simply programming the target recognition sequence in NS, only one crRNA is required to detect both nucleic acid and non-nucleic acid biomarkers. The detection limit decreased by ∼196-fold for miRNA-375 and 122-fold for prostate-specific antigen (PSA), respectively. Moreover, versatile evaluation of miRNA-375 and PSA in clinical urine samples can also be achieved, according to which prostate cancer and healthy groups can be well identified.