A Real-Time Small Target Vehicle Detection Algorithm with an Improved YOLOv5m Network Model

To address the challenges of high complexity,poor real-time performance,and low detection rates for small target vehicles in existing vehicle object detection algorithms,this paper proposes a real-time lightweight architecture based on You Only Look Once(YOLO)v5m.Firstly,a lightweight upsampling operator called Content-Aware Reassembly of Features(CARAFE)is introduced in the feature fusion layer of the network to maximize the extraction of deep-level features for small target vehicles,reducing the missed detection rate and false detection rate.Secondly,a new prediction layer for tiny targets is added,and the feature fusion network is redesigned to enhance the detection capability for small targets.Finally,this paper applies L1 regularization to train the improved network,followed by pruning and fine-tuning operations to remove redundant channels,reducing computational and parameter complexity and enhancing the detection efficiency of the network.Training is conducted on the VisDrone2019-DET dataset.The experimental results show that the proposed algorithmreduces parameters and computation by 63.8% and 65.8%,respectively.The average detection accuracy improves by 5.15%,and the detection speed reaches 47 images per second,satisfying real-time requirements.Compared with existing approaches,including YOLOv5m and classical vehicle detection algorithms,our method achieves higher accuracy and faster speed for real-time detection of small target vehicles in edge computing.

Real-time Rescue Target Detection Based on UAV Imagery for Flood Emergency Response

Timely acquisition of rescue target information is critical for emergency response after a flood disaster.Unmanned Aerial Vehicles(UAVs)equipped with remote sensing capabilities offer distinct advantages,including high-resolution imagery and exceptional mobility,making them well suited for monitoring flood extent and identifying rescue targets during floods.However,there are some challenges in interpreting rescue information in real time from flood images captured by UAVs,such as the complexity of the scenarios of UAV images,the lack of flood rescue target detection datasets and the limited real-time processing capabilities of the airborne on-board platform.Thus,we propose a real-time rescue target detection method for UAVs that is capable of efficiently delineating flood extent and identifying rescue targets(i.e.,pedestrians and vehicles trapped by floods).The proposed method achieves real-time rescue information extraction for UAV platforms by lightweight processing and fusion of flood extent extraction model and target detection model.The flood inundation range is extracted by the proposed method in real time and detects targets such as people and vehicles to be rescued based on this layer.Our experimental results demonstrate that the Intersection over Union(IoU)for flood water extraction reaches an impressive 80%,and the IoU for real-time flood water extraction stands at a commendable 76.4%.The information on flood stricken targets extracted by this method in real time can be used for flood emergency rescue.

Real-Time Detection and Instance Segmentation of Strawberry in Unstructured Environment

The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting robots.Real-time identification of strawberries in an unstructured envi-ronment is a challenging task.Current instance segmentation algorithms for strawberries suffer from issues such as poor real-time performance and low accuracy.To this end,the present study proposes an Efficient YOLACT(E-YOLACT)algorithm for strawberry detection and segmentation based on the YOLACT framework.The key enhancements of the E-YOLACT encompass the development of a lightweight attention mechanism,pyramid squeeze shuffle attention(PSSA),for efficient feature extraction.Additionally,an attention-guided context-feature pyramid network(AC-FPN)is employed instead of FPN to optimize the architecture’s performance.Furthermore,a feature-enhanced model(FEM)is introduced to enhance the prediction head’s capabilities,while efficient fast non-maximum suppression(EF-NMS)is devised to improve non-maximum suppression.The experimental results demonstrate that the E-YOLACT achieves a Box-mAP and Mask-mAP of 77.9 and 76.6,respectively,on the custom dataset.Moreover,it exhibits an impressive category accuracy of 93.5%.Notably,the E-YOLACT also demonstrates a remarkable real-time detection capability with a speed of 34.8 FPS.The method proposed in this article presents an efficient approach for the vision system of a strawberry-picking robot.

Real-Time Object Detection and Face Recognition Application for the Visually Impaired

The advancement of navigation systems for the visually impaired has significantly enhanced their mobility by mitigating the risk of encountering obstacles and guiding them along safe,navigable routes.Traditional approaches primarily focus on broad applications such as wayfinding,obstacle detection,and fall prevention.However,there is a notable discrepancy in applying these technologies to more specific scenarios,like identifying distinct food crop types or recognizing faces.This study proposes a real-time application designed for visually impaired individuals,aiming to bridge this research-application gap.It introduces a system capable of detecting 20 different food crop types and recognizing faces with impressive accuracies of 83.27%and 95.64%,respectively.These results represent a significant contribution to the field of assistive technologies,providing visually impaired users with detailed and relevant information about their surroundings,thereby enhancing their mobility and ensuring their safety.Additionally,it addresses the vital aspects of social engagements,acknowledging the challenges faced by visually impaired individuals in recognizing acquaintances without auditory or tactile signals,and highlights recent developments in prototype systems aimed at assisting with face recognition tasks.This comprehensive approach not only promises enhanced navigational aids but also aims to enrich the social well-being and safety of visually impaired communities.

Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time

Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and speed.This research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image sequence.An annotated image dataset trains the proposed system and predicts the camera pose in real-time.The system mainly improved the localization performance of indoor cameras by more accurately predicting the camera pose.It also recognizes the scene changes during the sequence and evaluates the effects of these changes.This system achieved high accuracy and real-time performance.The scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact evaluation.Overall,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.

A CNN-Based Single-Stage Occlusion Real-Time Target Detection Method

Aiming at the problem of low accuracy of traditional target detection methods for target detection in endoscopes in substation environments, a CNN-based real-time detection method for masked targets is proposed. The method adopts the overall design of backbone network, detection network and algorithmic parameter optimisation method, completes the model training on the self-constructed occlusion target dataset, and adopts the multi-scale perception method for target detection. The HNM algorithm is used to screen positive and negative samples during the training process, and the NMS algorithm is used to post-process the prediction results during the detection process to improve the detection efficiency. After experimental validation, the obtained model has the multi-class average predicted value (mAP) of the dataset. It has general advantages over traditional target detection methods. The detection time of a single target on FDDB dataset is 39 ms, which can meet the need of real-time target detection. In addition, the project team has successfully deployed the method into substations and put it into use in many places in Beijing, which is important for achieving the anomaly of occlusion target detection.

Advancing PCB Quality Control:Harnessing YOLOv8 Deep Learning for Real-Time Fault Detection

Printed Circuit Boards(PCBs)are materials used to connect components to one another to form a working circuit.PCBs play a crucial role in modern electronics by connecting various components.The trend of integrating more components onto PCBs is becoming increasingly common,which presents significant challenges for quality control processes.Given the potential impact that even minute defects can have on signal traces,the surface inspection of PCB remains pivotal in ensuring the overall system integrity.To address the limitations associated with manual inspection,this research endeavors to automate the inspection process using the YOLOv8 deep learning algorithm for real-time fault detection in PCBs.Specifically,we explore the effectiveness of two variants of the YOLOv8 architecture:YOLOv8 Small and YOLOv8 Nano.Through rigorous experimentation and evaluation of our dataset which was acquired from Peking University’s Human-Robot Interaction Lab,we aim to assess the suitability of these models for improving fault detection accuracy within the PCB manufacturing process.Our results reveal the remarkable capabilities of YOLOv8 Small models in accurately identifying and classifying PCB faults.The model achieved a precision of 98.7%,a recall of 99%,an accuracy of 98.6%,and an F1 score of 0.98.These findings highlight the potential of the YOLOv8 Small model to significantly improve the quality control processes in PCB manufacturing by providing a reliable and efficient solution for fault detection.

Real-Time Fraud Detection Using Machine Learning

Credit card fraud remains a significant challenge, with financial losses and consumer protection at stake. This study addresses the need for practical, real-time fraud detection methodologies. Using a Kaggle credit card dataset, I tackle class imbalance using the Synthetic Minority Oversampling Technique (SMOTE) to enhance modeling efficiency. I compare several machine learning algorithms, including Logistic Regression, Linear Discriminant Analysis, K-nearest Neighbors, Classification and Regression Tree, Naive Bayes, Support Vector, Random Forest, XGBoost, and Light Gradient-Boosting Machine to classify transactions as fraud or genuine. Rigorous evaluation metrics, such as AUC, PRAUC, F1, KS, Recall, and Precision, identify the Random Forest as the best performer in detecting fraudulent activities. The Random Forest model successfully identifies approximately 92% of transactions scoring 90 and above as fraudulent, equating to a detection rate of over 70% for all fraudulent transactions in the test dataset. Moreover, the model captures more than half of the fraud in each bin of the test dataset. SHAP values provide model explainability, with the SHAP summary plot highlighting the global importance of individual features, such as “V12” and “V14”. SHAP force plots offer local interpretability, revealing the impact of specific features on individual predictions. This study demonstrates the potential of machine learning, particularly the Random Forest model, for real-time credit card fraud detection, offering a promising approach to mitigate financial losses and protect consumers.

Real-time image processing and display in object size detection based on VC++

Real-time detection for object size has now become a hot topic in the testing field and image processing is the core algorithm. This paper focuses on the processing and display of the collected dynamic images to achieve a real-time image pro- cessing for the moving objects. Firstly, the median filtering, gain calibration, image segmentation, image binarization, cor- ner detection and edge fitting are employed to process the images of the moving objects to make the image close to the real object. Then, the processed images are simultaneously displayed on a real-time basis to make it easier to analyze, understand and identify them, and thus it reduces the computation complexity. Finally, human-computer interaction （HCI）-friendly in- terface based on VC ＋＋ is designed to accomplish the digital logic transform, image processing and real-time display of the objects. The experiment shows that the proposed algorithm and software design have better real-time performance and accu- racy which can meet the industrial needs.

FPGA implementation of 500-MHz high-count-rate high-time-resolution real-time digital neutron-gamma discrimination for fast liquid detectors

Fast neutron flux measurements with high count rates and high time resolution have important applications in equipment such as tokamaks.In this study,real-time neutron and gamma discrimination was implemented on a self-developed 500-Msps,12-bit digitizer,and the neutron and gamma spectra were calculated directly on an FPGA.A fast neutron flux measurement system with BC-501A and EJ-309 liquid scintillator detectors was developed and a fast neutron measurement experiment was successfully performed on the HL-2 M tokamak at the Southwestern Institute of Physics,China.The experimental results demonstrated that the system obtained the neutron and gamma spectra with a time accuracy of 1 ms.At count rates of up to 1 Mcps,the figure of merit was greater than 1.05 for energies between 50 keV and 2.8 MeV.

A fast and adaptive method for automatic weld defect detection in various real-time X-ray imaging systems

A first and effective method is proposed to detect weld deject adaptively in various Dypes of real-time X-ray images obtained in different conditions. After weld extraction and noise reduction, a proper template of median filter is used to estimate the weld background. After the weld background is subtracted from the original image, an adaptite threshold segmentation algorithm is proposed to obtain the binary image, and then the morphological close and open operation, labeling algorithm and fids＇e alarm eliminating algorithm are applied to pracess the binary image to obtain the defect, ct detection result. At last, a fast realization procedure jbr proposed method is developed. The proposed method is tested in real-time X-ray image,s obtairted in different X-ray imaging sutems. Experiment results show that the proposed method is effective to detect low contrast weld dejects with few .false alarms and is adaptive to various types of real-time X-ray imaging systems.

Real-Time Detection of Cracks on Concrete Bridge Decks Using Deep Learning in the Frequency Domain

This paper presents a vision-based crack detection approach for concrete bridge decks using an integrated one-dimensional convolutional neural network(1D-CNN)and long short-term memory(LSTM)method in the image frequency domain.The so-called 1D-CNN-LSTM algorithm is trained using thousands of images of cracked and non-cracked concrete bridge decks.In order to improve the training efficiency,images are first transformed into the frequency domain during a preprocessing phase.The algorithm is then calibrated using the flattened frequency data.LSTM is used to improve the performance of the developed network for long sequence data.The accuracy of the developed model is 99.05%,98.9%,and 99.25%,respectively,for training,validation,and testing data.An implementation framework is further developed for future application of the trained model for large-scale images.The proposed 1D-CNN-LSTM method exhibits superior performance in comparison with existing deep learning methods in terms of accuracy and computation time.The fast implementation of the 1D-CNN-LSTM algorithm makes it a promising tool for real-time crack detection.

Real-time Fluorescence PCR Method for Detection of Burkholderia glumae from Rice

Burkholderia glumae causing seedling rot and grain rot of rice was listed as a plant quarantine disease of China in 2007. It＇s quite necessary to set up effective detection methods for the pathogen to manage further dispersal of this disease. The present study combined the real-time PCR method with classical PCR to increase the detecting efficiency, and to develop an accurate, rapid and sensitive method to detect the pathogen in the seed quarantine for effective management of the disease. The results showed that all the tested strains of B. glumae produced about 139 bp specific fragments by the real-time PCR and the general PCR methods, while others showed negative PCR result. The bacteria could be detected at the concentrations of 1×10^4 CFU/mL by general PCR method and at the concentrations below 100 CFU/mL by real-time fluorescence PCR method. B. glumae could be detected when the inoculated and healthy seeds were mixed with a proportion of 1：100.

Development and validation of a TaqMan^(TM) fluorescent quantitative real-time PCR assay for the rapid detection of Edwardsiella tarda

Edwardsiella tarda is one of the most important emerging pathogens in tile global aquaculture industries. As such, an accurate diagnosis and quantitative analytical methods are urgently needed for this bacterium. In this study, primers and a TaqMan probe specific to the conservative sequences of the 16S rRNA gene of E. tarda were designed. The concentration of primers and TaqMan probe were optimized to 200 nmol/L and 120 nmol/L, respectively. The detection sensitivity of the FQ- PCR assay was determined to be as low as five copies of the target sequence per reaction using the pGEM-16S rDNA recombinant plasmid as a template, which was 100 times more sensitive than conventional PCR. A standard curve by plotting the threshold cycle values （y） against the common logarithmic copies （logl0n~ as x; n~ is copy number） of pGEM-16S rDNA was generated. The results of intra- and inter-assay variability tests demonstrate that the established FQ-PCR method was highly reproducible. The assay was specific for E. tarda as it showed that there was no cross-reactivity to eight additional bacterial pathogen strains in aquaculture. Thus, the FQ-PCR assay has the potential for diagnostic purposes and for other applications, especially for the rapid detection and quantification of low-grade E. tarda infections.

Comparison of ligase detection reaction and real-time PCR for detection of low abundant YMDD mutants in patients with chronic hepatitis B

AIM: To compare the ligase detection reaction (LDR) and real-time PCR for detection of low abundant YMDD mutants in patients with chronic hepatitis B infection.METHODS: Mixtures of plasmids and serum samples from 52 chronic hepatitis B patients with low abundant lamivudine-resistant mutations were tested with LDR and real-time PCR. Time required and reagent cost for both assays were evaluated.RESULTS: Real-time PCR detected 100, 50, 10, 1 and 0.1% of YIDD plasmid, whereas LDR detected 100, 50, 10, 1, 0.1, and 0.01% of YIDD plasmid, in mixtures with YMDD plasmid of 106 copies/mL. Among the 52 clinical serum samples, completely concordant results were obtained for all samples by both assays, and 39 YIDD, 9 YVDD, and 4 YIDD/YVDD were detected. Cost and time required for LDR and real-time PCR are 60/80 CNY (8/10.7 US dollars) and 4.5/2.5 h, respectively.CONCLUSION: LDR and real-time PCR are both sensitive and inexpensive methods for monitoring low abundant YMDD mutants during lamivudine therapy in patients with chronic hepatitis B. LDR is more sensitive and less expensive, while real-time PCR is more rapid.

Detection of let-7a microRNA by real-time PCR in gastric carcinoma

AIM: To establish an accurate and rapid stem-loop reverse transcriptional real-time PCR (RT-PCR) method to quantify human let-7a miRNA in gastric cancer. METHODS: According to the sequence of let-7a miRNA,the stem-loop reverse transcriptional primer,the primers and quantitative MGB probes of real-time PCR were designed and synthesized. The dynamic range and the sensitivity of quantitative reverse transcriptional real-time PCR were determined. The levels of let-7a miRNA were examined in 32 gastric carcinoma samples by stem-loop RT-PCR method. RESULTS: The dynamic range and sensitivity of the let-7a miRNA quantification scheme were evaluated,the result showed the assay could precisely detect 10 copies of mature let-7a miRNA in as few as 0.05 ng of total RNA of gastric mucosa. The results of specificity analysis showed no fluorescence signal occurred even though 50 ng of human genomic DNA was added to the reverse transcription (RT) reaction. The expression level of let-7a miRNA in gastric tumor tissues was significantly lower compared to normal tissues in 14 samples from 32 patients. CONCLUSION: The stem-loop RT-PCR is a reliable method to detect let-7a miRNA which may play an important role in the development of gastric carcinoma.

A real-time PCR targeted to the upstream regions of HlyB for specific detection of Edwardsiella tarda

Edwardsiella tarda has become one of the most important emerging pathogens in aquaculture industry. Therefore, a rapid, reproducible, and sensitive method for detection and quantification of this pathogen is needed urgently. To achieve this purpose, we developed a TaqMan-based real-time PCR assay for detection and quantification orE. tarda. The assay targets the hemolysin activator HlyB domain protein of E. tarda. Our optimized TaqMan assay is capable of detecting as little as 40 fg of genomic DNA per reaction. A standard curve was generated from the threshold cycle values （y） against log10 （E. tarda genomic DNA concentration） as x. The intra- and inter-assay coefficient of variation （CV） values were less than 2.06% and 1.05% respectively, indicating that the assay had good reproducibility. This method is highly specific to E. tarda strains, as it shows no cross-reactivity to Edwardsiella ictaluri, a member of the same genus, or to nine other fish-pathogenic bacteria species belonging to three other genera. This sensitive and specific real-time PCR assay provides a valuable tool for diagnostic quantitation of E. tarda in clinical samples.

Self-Powered Implantable Skin-Like Glucometer for Real-Time Detection of Blood Glucose Level In Vivo

Implantable bioelectronics for analyzing physiological biomarkers has recently been recognized as a promising technique in medical treatment or diagnostics. In this study, we developed a self-powered implantable skinlike glucometer for real-time detection of blood glucose level in vivo. Based on the piezo-enzymatic-reaction coupling effect of GOx@ZnO nanowire, the device under an applied deformation can actively output piezoelectric signal containing the glucose-detecting information. No external electricity power source or battery is needed for this device, and the outputting piezoelectric voltage acts as both the biosensing signal and electricity power. A practical application of the skin-like glucometer implanted in mouse body for detecting blood glucose level has been simply demonstrated. These results provide a new technique path for diabetes prophylaxis and treatment.

Real-time moving object detection for video monitoring systems

Moving object detection is one of the challenging problems in video monitoring systems, especially when the illumination changes and shadow exists. Amethod for real-time moving object detection is described. Anew background model is proposed to handle the illumination varition problem. With optical flow technology and background subtraction, a moving object is extracted quickly and accurately. An effective shadow elimination algorithm based on color features is used to refine the moving obj ects. Experimental results demonstrate that the proposed method can update the background exactly and quickly along with the varition of illumination, and the shadow can be eliminated effectively. The proposed algorithm is a real-time one which the foundation for further object recognition and understanding of video mum＇toting systems.

Real-time RT-PCR Assay for the detection of Tahyna Virus

A real-time RT-PCR （RT-qPCR） assay for the detection of Tahyna virus was developed to monitor Tahyna virus infection in field-collected vector mosquito samples. The targets selected for the assay were S segment sequences encoding the nucleocapsid protein from the Tahyna virus. Primers and probes were selected in conserved regions by aligning genetic sequences from various Tahyna virus strains available from GenBank. The sensitivity of the RT-qPCR approach was compared to that of a standard plaque assay in BHK cells. RT-qPCR assay can detect 4.8 PFU of titrated Tahyna virus. Assay specificities were determined by testing a battery of arboviruses, including representative strains of Tahyna virus and other arthropod-borne viruses from China. Seven strains of Tahyna virus were confirmed as positive; the other seven species of arboviruses could not be detected by RT-qPCR. Additionally, the assay was used to detect Tahyna viral RNA in pooled mosquito samples. The RT-qPCR assay detected Tahyna virus in a sensitive, specific, and rapid manner; these findings support the use of the assay in viral surveillance.