Improve Detection Performance Research Articles

Silica Nanowires-Filled Glass Microporous Sensor for the Ultrasensitive Detection of Deoxyribonucleic Acid.

DNA carries genetic information and can serve as an important biomarker for the early diagnosis and assessment of the disease prognosis. Here, we propose a bottom-up assembly method for a silica nanowire-filled glass microporous (SiNWs@GMP) sensor and develop a universal sensing platform for the ultrasensitive and specific detection of DNA. The three-dimensional network structure formed by SiNWs provides them with highly abundant and accessible binding sites, allowing for the immobilization of a large amount of capture probe DNA, thereby enabling more target DNA to hybridize with the capture probe DNA to improve detection performance. Therefore, the SiNWs@GMP sensor achieves ultrasensitive detection of target DNA. In the detection range of 1 aM to 100 fM, there is a good linear relationship between the decrease rate of current signal and the concentration of target DNA, and the detection limit is as low as 1 aM. The developed SiNWs@GMP sensor can distinguish target DNA sequences that are 1-, 3-, and 5-mismatched, and specifically recognize target DNA from complex mixed solution. Furthermore, based on this excellent selectivity and specificity, we validate the universality of this sensing strategy by detecting DNA (H1N1 and H5N1) sequences associated with the avian influenza virus. By replacing the types of nucleic acid aptamers, it is expected to achieve a wide range and low detection limit sensitive detection of various biological molecules. The results indicate that the developed universal sensing platform has ultrahigh sensitivity, excellent selectivity, stability, and acceptable reproducibility, demonstrating its potential application in DNA bioanalysis.

Multi-YOLOv8: An infrared moving small object detection model based on YOLOv8 for air vehicle

The detection of infrared moving small objects faces significant challenges in the field of object detection for air vehicles. These types of objects usually occupy a small number of pixels in an infrared image, resulting in limited feature information, considerable feature loss, low recognition accuracy, and various challenges in single-frame detection. To address these challenges, this paper proposes an efficient multi-input method named Multi-YOLOv8, which is based on the YOLOv8s model. The proposed method uses current frames as a primary input and incorporates optical flow processing images and background suppression images as auxiliary inputs to improve detection performance. In addition, an improved method is developed for optical flow computations, named the pyramidal weight-momentum Horn–Schunck (PWMHS) method, which can process optical flows efficiently and precisely. An improved version of the Wise-IoU (WIoU) v3, referred to as α*-WIoU v3, is proposed as a bounding box regression (BBR) loss function to optimize the YOLOv8 network. Further, the BiFormer module and lightweight convolution GSConv are introduced to improve the attention to key information for the objects and balance the computational cost and detection performance, respectively. Moreover, a small object detection layer is added the YOLOv8 network to improve the capability for small object detection. Finally, a warming-up training method that can reduce the dependency on auxiliary inputs and ensure model stability in case of auxiliary input failures is developed. The results of the comprehensive experiments on an open-access dataset reveal that the proposed model outperforms the mainstream models in overall performance. The proposed method can significantly enhance the detection ability of infrared moving small objects.

Multicolor-Assay-on-a-Chip Processed by Robotic Operation (MACpro) with Improved Diagnostic Accuracy for Field-Deployable Detection.

The ability to deploy decentralized laboratories with autonomous and reliable disease diagnosis holds the potential to deliver accessible healthcare services for public safety. While microfluidic technologies provide precise manipulation of small fluid volumes with improved assay performance, their limited automation and versatility confine them to laboratories. Herein, we report the utility of multicolor assay-on-a-chip processed by robotic operation (MACpro), to address this unmet need. The MACpro platform comprises a robot-microfluidic interface and an eye-in-hand module that provides flexible yet stable actions to execute tasks in a programmable manner, such as the precise manipulation of the microfluidic chip along with different paths. Notably, MACpro shows improved detection performance by integrating the microbead-based antibody immobilization with enhanced target recognition and multicolor sensing via Cu2+-catalyzed plasmonic etching of gold nanorods for rapid and sensitive analyte quantification. Using interferon-gamma as an example, we demonstrate that MACpro completes a sample-to-answer immunoassay within 30 min and achieves a 10-fold broader dynamic range and a 10-fold lower detection limit compared to standard enzyme-linked immunosorbent assays (0.66 vs 5.2 pg/mL). MACpro extends the applications beyond traditional laboratories and presents an automated solution to expand diagnostic capacity in diverse settings.

Detection of Pine-Wilt-Disease-Affected Trees Based on Improved YOLO v7

Pine wilt disease (PWD) poses a significant threat to global pine resources because of its rapid spread and management challenges. This study uses high-resolution helicopter imagery and the deep learning model You Only Look Once version 7 (YOLO v7) to detect symptomatic trees in forests. Attention mechanism technology from artificial intelligence is integrated into the model to enhance accuracy. Comparative analysis indicates that the YOLO v7-SE model exhibited the best performance, with a precision rate of 0.9281, a recall rate of 0.8958, and an F1 score of 0.9117. This study demonstrates efficient and precise automatic detection of symptomatic trees in forest areas, providing reliable support for prevention and control efforts, and emphasizes the importance of attention mechanisms in improving detection performance.

YOLO-MSRF for lung nodule detection

(Aim) Aiming at the problem that there are a large number of small object nodules that are difficult to detect in lung images, detection methods based on improved YOLOv7 are proposed in this paper. (Method) First, a new small object detection layer (SODL) is proposed to solve the problem of the small size and irregular shape of lung nodules being difficult to detect accurately. Secondly, aiming at the problem that the characteristics of lung nodules are blurred and difficult to detect due to the continuous downsampling of the model, a multi-scale receptive field (MSRF) module is proposed and designed to improve the model's extraction of channel features. Finally, efficient omni-dimensional convolution (EODConv) is used to improve the ability of the network to extract the space, filters, and channels of the convolution kernel. (Results) Experiments were carried out on the public Luna16 dataset, and the results showed that our mAP, precision, and recall rate reached 95.26 %, 95.41 %, and 94.02 %, respectively, surpassing many state-of-the-art models. (Conclusion) In this study, a YOLOv7-based method is proposed for detecting lung nodules. Experimental results show that the proposed modification can significantly improve detection performance and is more suitable for clinical medical diagnosis.

Improved Re-Parameterized Convolution for Wildlife Detection in Neighboring Regions of Southwest China.

To autonomously detect wildlife images captured by camera traps on a platform with limited resources and address challenges such as filtering out photos without optimal objects, as well as classifying and localizing species in photos with objects, we introduce a specialized wildlife object detector tailored for camera traps. This detector is developed using a dataset acquired by the Saola Working Group (SWG) through camera traps deployed in Vietnam and Laos. Utilizing the YOLOv6-N object detection algorithm as its foundation, the detector is enhanced by a tailored optimizer for improved model performance. We deliberately introduce asymmetric convolutional branches to enhance the feature characterization capability of the Backbone network. Additionally, we streamline the Neck and use CIoU loss to improve detection performance. For quantitative deployment, we refine the RepOptimizer to train a pure VGG-style network. Experimental results demonstrate that our proposed method empowers the model to achieve an 88.3% detection accuracy on the wildlife dataset in this paper. This accuracy is 3.1% higher than YOLOv6-N, and surpasses YOLOv7-T and YOLOv8-N by 5.5% and 2.8%, respectively. The model consistently maintains its detection performance even after quantization to the INT8 precision, achieving an inference speed of only 6.15 ms for a single image on the NVIDIA Jetson Xavier NX device. The improvements we introduce excel in tasks related to wildlife image recognition and object localization captured by camera traps, providing practical solutions to enhance wildlife monitoring and facilitate efficient data acquisition. Our current work represents a significant stride toward a fully automated animal observation system in real-time in-field applications.

AFENet: Attention-guided feature enhancement network and a benchmark for low-altitude UAV sewage outfall detection

Inspecting sewage outfall into rivers is significant to the precise management of the ecological environment because they are the last gate for pollutants to enter the river. Unmanned Aerial Vehicles (UAVs) have the characteristics of maneuverability and high-resolution images and have been used as an important means to inspect sewage outfalls. UAVs are widely used in daily sewage outfall inspections, but relying on manual interpretation lacks the corresponding low-altitude sewage outfall images dataset. Meanwhile, because of the sparse spatial distribution of sewage outfalls, problems like less labeled sample data, complex background types, and weak objects are also prominent. In order to promote the inspection of sewage outfalls, this paper proposes a low-attitude sewage outfall object detection dataset, namely UAV-SOD, and an attention-guided feature enhancement network, namely AFENet. The UAV-SOD dataset features high resolution, complex backgrounds, and diverse objects. Some of the outfall objects are limited by multi-scale, single-colored, and weak feature responses, leading to low detection accuracy. To localize these objects effectively, AFENet first uses the global context block (GCB) to jointly explore valuable global and local information, and then the region of interest (RoI) attention module (RAM) is used to explore the relationships between RoI features. Experimental results show that the proposed method improves detection performance on the proposed UAV-SOD dataset than representative state-of-the-art two-stage object detection methods.

Multifunctional Optical Sensing with Lanthanide-Doped Upconverting Nanomaterials: Improving Detection Performance of Temperature and Pressure in the Visible and NIR Ranges.

Temperature and pressure are fundamental physical parameters in the field of materials science, making their monitoring of utmost significance for scientists and engineers. Here, the NaSrY(MoO4)3:0.02Er3+/0.01Tm3+/0.15Yb3+ nanophosphor is developed as an optical sensor material. Under 975 nm laser excitation, the upconversion characteristics and optical detection performance of the multifunctional sensing platform of temperature and pressure (vacuum) are investigated. We have successfully developed a novel detection platform that enables optical detection of pressure (vacuum) and temperature. This platform utilizes thermally coupled levels (TCLs) and non-TCLs of Er3+ and Tm3+ to achieve ratiometric detection. The multimodal optical temperature and pressure detection based on TCLs and non-TCLs is successfully realized by using different emission bands of double emission centers, which makes it possible for self-referencing optical temperature and pressure measurement modes. These results indicate that the developed nanophosphor is a promising candidate for optical sensors, and our findings suggest potential strategies for modulating the sensor properties of luminescent materials doped with rare-earth ions.

A High‐Performance Phosphate Potentiometric Sensor Based on Cobalt Nanoparticles Electroplated on Hierarchically Porous Laser‐Induced Graphene

AbstractPhosphate is an important fertilizer in agriculture and electrochemical sensors based on cobalt (Co) are promising for measurement of phosphate. The deposition of Co onto conductive substrates to form a nano/microstructured Co layer with rich active sites is a typical strategy to improve detection performances. However, the widely used substrates show smooth surfaces, which cannot provide extra active sites. Herein, a high‐performance phosphate sensor is fabricated by generating Co nanoparticles (NPs) on a hierarchically porous laser‐induced graphene (LIG) matrix with a large surface area. Specifically, a Co layer is first electroplated on the LIG matrix. Nevertheless, the pores of LIG will be blocked by the dense Co layer so the performances of the fabricated LIG‐Co sensor exhibit limited enhancements. Therefore, a “nearly all etching (NAE)” technique is developed to transform the dense Co layer to dispersive Co NPs and re‐expose the pores of LIG, both increasing the active sites. Compared to the LIG‐Co sensor, the LIG‐Co NP sensor displays overall superiority. The response time, sensitivity, and linearity are enhanced from ≈60 to ≈10 s, from 0.012 to 0.069 V dec−1, and from 0.973 to 0.998, respectively. The LIG‐Co NP sensor also shows a decent anti‐interference capability.

Optic disc and cup segmentation for glaucoma detection using Attention U-Net incorporating residual mechanism.

Glaucoma is a common eye disease that can cause blindness. Accurate detection of the optic disc and cup disc is crucial for glaucoma diagnosis. Algorithm models based on artificial intelligence can assist doctors in improving detection performance. In this article, U-Net is used as the backbone network, and the attention and residual modules are integrated to construct an end-to-end convolutional neural network model for optic disc and cup disc segmentation. The U-Net backbone is used to infer the basic position information of optic disc and cup disc, the attention module enhances the model's ability to represent and extract features of optic disc and cup disc, and the residual module alleviates gradient disappearance or explosion that may occur during feature representation of the neural network. The proposed model is trained and tested on the DRISHTI-GS1 dataset. Results show that compared with the original U-Net method, our model can more effectively separate optic disc and cup disc in terms of overlap error, sensitivity, and specificity.

Advancements in Deep-Learning-Based Object Detection in Challenging Environments

This article focuses on object detection in challenging environments, where objects of interest need to be detected in images captured under unconstrained conditions. These environments can include outdoor scenes with varying lighting, weather conditions, and background clutter. Object detection in such scenarios is crucial for applications like autonomous driving, surveillance, and industrial production inspection. The paper explores existing techniques for object detection in challenging environments and proposes novel solutions to enhance its performance. One key aspect emphasized in the paper is the need for relevant datasets to validate models under unconstrained scenarios. While several datasets already exist, the research identifies gaps in specific situations and addresses them by proposing new datasets, such as the Indian Lane Dataset for autonomous vehicles. The proposed object detection framework leverages spatial information to detect objects in challenging environments, and its performance is evaluated against benchmark datasets and the newly proposed datasets. A real-world application demonstrates significant improvements in object detection performance in natural environments.

Research on lightweight algorithm for gangue detection based on improved Yolov5

In order to solve the problems of slow detection speed, large number of parameters and large computational volume of deep learning based gangue target detection method, we propose an improved algorithm for gangue target detection based on Yolov5s. First, the lightweight network EfficientVIT is used as the backbone network to increase the target detection speed. Second, C3_Faster replaces the C3 part in the HEAD module, which reduces the model complexity. once again, the 20 × 20 feature map branch in the Neck region is deleted, which reduces the model complexity; thirdly, the CIOU loss function is replaced by the Mpdiou loss function. The introduction of the SE attention mechanism makes the model pay more attention to critical features to improve detection performance. Experimental results show that the improved model size of the coal gang detection algorithm reduces the compression by 77.8%, the number of parameters by 78.3% the computational cost is reduced by 77.8% and the number of frames is reduced by 30.6%, which can be used as a reference for intelligent coal gangue classification.

A high-performance terahertz detector based on a graded channel GaN high-electron-mobility transistor with a recessed gate

A novel recessed gate graded AlGaN high-electron-mobility transistor (RGGA HEMT) for high-sensitivity terahertz (THz) detectors is reported, which exhibits significant potential for high-sensitivity THz detectors, and consequently. The simulation results indicate the exceptional field-effect factor of the proposed RGGA HEMT, which is largely attributed to the three-dimensional electron gas generated by the graded AlGaN barrier layer and the subsequent enhancement of channel conductance. The responsivity and noise equivalent power reach 58mA/W and 43pW/Hz respectively at 227GHz by utilizing a technical computer-aided design platform. As compared to conventional structure GaN HEMT experimental data, the current responsivity is improved by 2.64 times, and the NEP is reduced by 43.4%. The unprecedented improvement in detection performance renders the RGGA HEMT a highly promising approach for future THz real-time single-pixel imaging applications at room temperature.

DeepFake detection based on high-frequency enhancement network for highly compressed content

The DeepFake, which generates synthetic content, has sparked a revolution in the fight against deception and forgery. However, most existing DeepFake detection methods mainly focus on improving detection performance with high-quality data while ignoring low-quality synthetic content that suffers from high compression. To address this issue, we propose a novel High-Frequency Enhancement framework, which leverages a learnable adaptive high-frequency enhancement network to enrich weak high-frequency information in compressed content without uncompressed data supervision. The framework consists of three branches, i.e., the Basic branch with RGB domain, the Local High-Frequency Enhancement branch with Block-wise Discrete Cosine Transform, and the Global High-Frequency Enhancement branch with Multi-level Discrete Wavelet Transform. Among them, the local branch utilizes the Discrete Cosine Transform coefficient and channel attention mechanism to indirectly achieve adaptive frequency-aware multi-spatial attention, while the global branch supplements the high-frequency information by extracting coarse-to-fine multi-scale high-frequency cues and cascade-residual-based multi-level fusion by Discrete Wavelet Transform coefficients. In addition, we design a Two-Stage Cross-Fusion module to effectively integrate all information, thereby greatly enhancing weak high-frequency information in low-quality data. Experimental results on FaceForensics++, Celeb-DF, and OpenForensics datasets show that the proposed method outperforms the existing state-of-the-art methods and can effectively improve the detection performance of DeepFakes, especially on low-quality data. The code is available here.11https://github.com/Gina-YUE/HiFE.

BEV-CFKT: A LiDAR-camera cross-modality-interaction fusion and knowledge transfer framework with transformer for BEV 3D object detection

The BEV-CFKT proposed in this paper leverages knowledge transfer through transformers for LiDAR-Camera fusion in the Bird’s-Eye-View (BEV) space, aiming to achieve accurate and robust 3D object detection. BEV-CFKT comprises three main components, which include the generation of BEV features from images and point clouds, cross-modality interaction, and hybrid object queries using a monocular detection head. By unifying features from both point clouds and images into the BEV space, we simplify modal interaction, facilitate knowledge transfer, and extract richer structural and semantic information from multimodal data. This effectively enhances the network’s performance. To further improve detection performance, BEV-CFKT incorporates a temporal fusion module. Additionally, a hybrid object queries module based on a monocular detection head accelerates the convergence of our model. We demonstrate the effectiveness of our approach through an extensive set of experiments.

Detection of an in-housed pig using modified YOLOv5 model

Object detection has made significant strides in recent years, but it remains a challenging task to accurately and quickly identify and detect objects. While humans can easily recognize objects in images or videos regardless of their appearance, computers face difficulties in this task. Object detection plays a crucial role in computer vision and finds applications in various domains such as healthcare, security, agriculture, home automation and more. To address the challenges of object detection, several techniques have been developed including RCNN, Faster RCNN, YOLO and Single Shot Detector (SSD). In this paper, we propose a modified YOLOv5s architecture that aims to improve detection performance. Our modified architecture incorporates the C3Ghost module along with the SPP and SPPF modules in the YOLOv5s backbone network. We also utilize the Adam and Stochastic Gradient Descent (SGD) optimizers. The paper also provides an overview of three major versions of the YOLO object detection model: YOLOv3, YOLOv4 and YOLOv5. We discussed their respective performance analyses. For our evaluation, we collected a database of pig images from the ICAR-National Research Centre on Pig farm. We assessed the performance using four metrics such as Precision (P), Recall (R), F1-score and mAP @ 0.50. The computational results demonstrate that our method YOLOv5s architecture achieves a 0.0414 higher mAP while utilizing less memory space compared to the original YOLOv5s architecture. This research contributes to the advancement of object detection techniques and showcases the potential of our modified YOLOv5s architecture for improved performance in real world applications.

The knowledge-aided generalized multipath adaptive detector

In urban environments, the challenges brought about by multipath propagation are significant for traditional adaptive radar detection problems. To tackle this issue, a generalized multipath signal model combined with environmental geometric prior information (EGPI) is developed. This model can be utilized regardless of whether the multipath signals are fully, partially, or partially-resolvable in the range domain. During the process of designing a detector using the criteria of the one-step generalized likelihood ratio test (GLRT), an attempt to find a closed-form expression for the maximum likelihood estimator of the unknown parameters was unsuccessful. However, by utilizing an adaptive configuration for the initial value, a cyclic optimization algorithm was able to obtain the estimated value within just a few iterations, demonstrating a rapid convergence rate. Furthermore, a detector based on the two-step GLRT criteria is proposed. Compared to traditional detectors, the newly developed detectors make more efficient use of the energy in multipath signals, resulting in improved detection performance. It has been demonstrated that when the whitened angles between subspaces are set to two specific values, the GLRT detectors exhibit the Constant False Alarm Probability (CFAR) property. Simulation results show that the false alarm probability(Pfa) at these two specific angles provides upper and lower bounds of Pfa.

An optimized outlier detection function for multibeam echo-sounder data

Multibeam sonar data are a valuable tool for seafloor mapping and geological studies. However, the presence of outliers in multibeam data can distort the results of analyses and reduce the accuracy of seafloor maps. In this paper, we define a weighting function based on the performance of various outlier detection techniques (ODTs) for detecting outliers in multibeam data, which calculates an outlier probability score for each sounding. Our results show that each ODT has its own strengths and weaknesses, and that a combination of outlier detection techniques is promising to improve reproducibility, explainability and the accuracy of the detection process. To address the challenge of detecting outliers in multibeam data, we propose a weighted outlier detection function that outperforms individual outlier detection techniques in terms of precision, recall and F1 scores by considering their strengths and combining them in a way that accounts for variations in the data. The function detects various types of outliers with high precision and recall values, resulting in valuable improvements in outlier detection performance for multibeam data. Overall, our proposed workflow has the potential to significantly improve the way multibeam data cleaning is performed, with the weighted outlier detection function being applied first, detecting most of the outlier automatically, followed by a domain-expert review of a small group of soundings whose automatic outlier labeling is not unequivocal.

Domain Adaptation of Anchor-Free object detection for urban traffic

Modern detectors are mostly trained under single and limited conditions. However, object detection faces various complex and open situations in autonomous driving, especially in urban street scenes with dense objects and complex backgrounds. Due to the shift in data distribution, modern detectors cannot perform well in actual urban environments. Using domain adaptation to improve detection performance is one of the key methods to extend object detection from limited situations to open situations. To this end, this article proposes a Domain Adaptation of Anchor-Free object detection (DAAF) for urban traffic. DAAF is a cross-domain object detection method that performs feature alignment including two aspects. On the one hand, we designed a fully convolutional adversarial training method for global feature alignment at the image level. Meanwhile, images can generally be decomposed into structural information and texture information. In urban street scenes, the structural information of images is generally similar. The main difference between the source domain and the target domain is texture information. Therefore, during global feature alignment, this paper proposes a method called texture information limitation (TIL). On the other hand, in order to solve the problem of variable aspect ratios of objects in urban street scenes, this article uses an anchor-free detector as the baseline detector. Since the anchor-free object detector can obtain neither explicit nor implicit instance-level features, we adopt Pixel-Level Adaptation (PLA) to align local features instead of instance-level alignment for local features. The size of the object has the greatest impact on the final detection effect, and the object scale in urban scenes is relatively rich. Guided by the differentiation of attention mechanisms, a multi-level adversarial network is designed to perform feature alignment of the output space at different feature levels called Scale Information Limitation (SIL). We conducted cross-domain detection experiments by using various urban streetscape autonomous driving object detection datasets, including adverse weather conditions, synthetic data to real data, and cross-camera adaptation. The experimental results indicate that the method proposed in this article is effective.

FAOT-Net: A 1.5-Stage Framework for 3D Pelvic Lymph Node Detection With Online Candidate Tuning.

Accurate and automatic detection of pelvic lymph nodes in computed tomography (CT) scans is critical for diagnosing lymph node metastasis in colorectal cancer, which in turn plays a crucial role in its staging, treatment planning, surgical guidance, and postoperative follow-up of colorectal cancer. However, achieving high detection sensitivity and specificity poses a challenge due to the small and variable sizes of these nodes, as well as the presence of numerous similar signals within the complex pelvic CT image. To tackle these issues, we propose a 3D feature-aware online-tuning network (FAOT-Net) that introduces a novel 1.5-stage structure to seamlessly integrate detection and refinement via our online candidate tuning process and takes advantage of multi-level information through the tailored feature flow. Furthermore, we redesign the anchor fitting and anchor matching strategies to further improve detection performance in a nearly hyperparameter-free manner. Our framework achieves the FROC score of 52.8 and the sensitivity of 91.7% with 16 false positives per scan on the PLNDataset. Code will be available at: github.com/SCUsomebody/FAOT-Net/.