Traditional Detection Methods Research Articles

A Semantic Spatial Structure-Based Loop Detection Algorithm for Visual Environmental Sensing

Loop closure detection is an important component of the Simultaneous Localization and Mapping (SLAM) algorithm, which is utilized in environmental sensing. It helps to reduce drift errors during long-term operation, improving the accuracy and robustness of localization. Such improvements are sorely needed, as conventional visual-based loop detection algorithms are greatly affected by significant changes in viewpoint and lighting conditions. In this paper, we present a semantic spatial structure-based loop detection algorithm. In place of feature points, robust semantic features are used to cope with the variation in the viewpoint. In consideration of the semantic features, which are region-based, we provide a corresponding matching algorithm. Constraints on semantic information and spatial structure are used to determine the existence of loop-back. A multi-stage pipeline framework is proposed to systematically leverage semantic information at different levels, enabling efficient filtering of potential loop closure candidates. To validate the effectiveness of our algorithm, we conducted experiments using the uHumans2 dataset. Our results demonstrate that, even when there are significant changes in viewpoint, the algorithm exhibits superior robustness compared to that of traditional loop detection methods.

FireXplainNet: Optimizing Convolution Block Architecture for Enhanced Wildfire Detection and Interpretability

The early detection of wildfires is a crucial challenge in environmental monitoring, pivotal for effective disaster management and ecological conservation. Traditional detection methods often fail to detect fires accurately and in a timely manner, resulting in significant adverse consequences. This paper presents FireXplainNet, a Convolutional Neural Network (CNN) base model, designed specifically to address these limitations through enhanced efficiency and precision in wildfire detection. We optimized data input via specialized preprocessing techniques, significantly improving detection accuracy on both the Wildfire Image and FLAME datasets. A distinctive feature of our approach is the integration of Local Interpretable Model-agnostic Explanations (LIME), which facilitates a deeper understanding of and trust in the model’s predictive capabilities. Additionally, we have delved into optimizing pretrained models through transfer learning, enriching our analysis and offering insights into the comparative effectiveness of FireXplainNet. The model achieved an accuracy of 87.32% on the FLAME dataset and 98.70% on the Wildfire Image dataset, with inference times of 0.221 and 0.168 milliseconds, respectively. These performance metrics are critical for the application of real-time fire detection systems, underscoring the potential of FireXplainNet in environmental monitoring and disaster management strategies.

Global structure graph mapping for multimodal change detection

ABSTRACT Multimodal change detection (MCD) combines multiple remote sensing data sources to realize surface change monitoring, which is essential for disaster evaluation and environmental monitoring. However, due to the ‘incomparable’ features in multimodal data, traditional change detection methods for unimodal (homogenous) data no longer apply. To address this issue, this paper proposes a novel MCD method with global structure graph mapping (GSGM) which extracts the ‘comparable’ structural features between multimodal datasets and constructs a global structure graph (GSG) to express the structure information for each of the multi-temporal images, which are then cross-mapped to the other data domain. The change intensity (CI) is determined by measuring the change of GSGs after mapping and the differences between GSGs and mapped GSGs. The forward and backward CI maps (CIMs) are then fused with the latent low-rank representation method (LLRR), and the change map (CM) is obtained by threshold segmentation. Experiments on five multimodal and four unimodal datasets demonstrate the effectiveness and robustness of the proposed method (source code is made available at https://github.com/rshante0426/GSGM).

Microfluidic Immunosensor Platform for Sensitive Detection of Human Epidermal Growth Factor Receptor-2 Based on Enhanced Cathode Electrochemiluminescence of Bimetallic Nanoclusters.

In this work, a microfluidic immunosensor chip was developed by incorporating microfluidic technology with electrochemiluminescence (ECL) for sensitive detection of human epidermal growth factor receptor-2 (HER2). The immunosensor chip can achieve robust reproducibility in mass production by integrating multiple detection units in a series. Notably, nanoscale materials can be better adapted to microfluidic systems, greatly enhancing the accuracy of the immunosensor chip. Ag@Au NCs closed by glutathione (GSH) were introduced in the ECL microfluidic immunosensor system with excellent and stable ECL performance. The synthesized CeO2-Au was applied as a coreaction promoter in the ECL signal amplification system, which made the result of HER2 detection more reliable. In addition, the designed microfluidic immunosensor chip integrated the biosensing system into a microchip, realizing rapid and accurate detection of HER2 by its high throughput and low usage. The developed short peptide ligand NARKFKG (NRK) achieved an effective connection between the antibody and nanocarrier for improving the detection efficiency of the sensor. The immunosensor chip had better storage stability and sensitivity than traditional detection methods, with a wide detection range from 10 fg·mL-1 to 100 ng·mL-1 and a low detection limit (LOD) of 3.29 fg·mL-1. In general, a microfluidic immunosensor platform was successfully constructed, providing a new idea for breast cancer (BC) clinical detection.

CoMadOut—a robust outlier detection algorithm based on CoMAD

AbstractUnsupervised learning methods are well established in the area of anomaly detection and achieve state of the art performances on outlier datasets. Outliers play a significant role, since they bear the potential to distort the predictions of a machine learning algorithm on a given dataset. Especially among PCA-based methods, outliers have an additional destructive potential regarding the result: they may not only distort the orientation and translation of the principal components, they also make it more complicated to detect outliers. To address this problem, we propose the robust outlier detection algorithm CoMadOut, which satisfies two required properties: (1) being robust towards outliers and (2) detecting them. Our CoMadOut outlier detection variants using comedian PCA define, dependent on its variant, an inlier region with a robust noise margin by measures of in-distribution (variant CMO) and optimized scores by measures of out-of-distribution (variants CMO*), e.g. kurtosis-weighting by CMO+k. These measures allow distribution based outlier scoring for each principal component, and thus, an appropriate alignment of the degree of outlierness between normal and abnormal instances. Experiments comparing CoMadOut with traditional, deep and other comparable robust outlier detection methods showed that the performance of the introduced CoMadOut approach is competitive to well established methods related to average precision (AP), area under the precision recall curve (AUPRC) and area under the receiver operating characteristic (AUROC) curve. In summary our approach can be seen as a robust alternative for outlier detection tasks.

The diagnostic value of metagenomics next-generation sequencing in HIV-infected patients with suspected pulmonary infections.

Traditional microbiological detection methods used to detect pulmonary infections in people living with HIV (PLHIV) are usually time-consuming and have low sensitivity, leading to delayed treatment. We aimed to evaluate the diagnostic value of metagenomics next-generation sequencing (mNGS) for microbial diagnosis of suspected pulmonary infections in PLHIV. We retrospectively analyzed PLHIV who were hospitalized due to suspected pulmonary infections at the sixth people hospital of Zhengzhou from November 1, 2021 to June 30, 2022. Bronchoalveolar lavage fluid (BALF) samples of PLHIV were collected and subjected to routine microbiological examination and mNGS detection. The diagnostic performance of the two methods was compared to evaluate the diagnostic value of mNGS for unknown pathogens. This study included a total of 36 PLHIV with suspected pulmonary infections, of which 31 were male. The reporting period of mNGS is significantly shorter than that of CMTs. The mNGS positive rate of BALF samples in PLHIV was 83.33%, which was significantly higher than that of smear and culture (44.4%, P<0.001). In addition, 11 patients showed consistent results between the two methods. Futhermore, mNGS showed excellent performance in identifying multi-infections in PLHIV, and 27 pathogens were detected in the BALF of 30 PLHIV by mNGS, among which 15 PLHIV were found to have multiple microbial infections (at least 3 pathogens). Pneumocystis jirovecii, human herpesvirus type 5, and human herpesvirus type 4 were the most common pathogen types. For PLHIV with suspected pulmonary infections, mNGS is capable of rapidly and accurately identifying the pathogen causing the pulmonary infection, which contributes to implement timely and accurate anti-infective treatment.

Early Detection System for Gas Leakage and Fire in Smart Home Using IOT

Gas leakage is an important environmental, commercial and residential problem that threatens human health, safety and the environment. Traditional gas detection methods often lack the ability to track time and do not provide timely information, leading to accidents and damage. In this context, Internet of Things (IoT) technology has been found to provide a reliable solution for continuous monitoring and remote management of gas detection systems. This paper presents an IoT-based gas leak detection system to improve safety and reduce the risk of gas leakage. leak. The proposed system integrates gas sensors, microcontrollers, communication modules and cloud computing infrastructure to enable real-time monitoring, data analysis and remote monitoring. Gas detectors used in designated areas indicated the presence of hazardous gases, and data was collected directly from the 's central processing unit for analysis. The system provides rapid rescue and intervention by sending instant notifications via mobile applications in case of a gas leak. The main features of the proposed system are scalability, flexibility and interoperability, allowing integration with existing infrastructure and integration of different protocols. Additionally, the use of a cloud-based platform facilitates data storage, analysis and visualization, allowing partners to gain insight intogasleaks, processes and possible consequences

URL Detection Based on Quantum Long Short-Term Memory Neural Network

In today's era, with the continuous development of the internet, surfing the internet has become an inseparable part of our lives. For those who frequently roam the internet, URLs are information that we are constantly exposed to. And YRL is also divided into malicious and normal. Clicking on malicious URLs can cause certain losses to our internet tools, and in severe cases, it can even lead to money theft. Malicious URL detection is an important task in the field of network security, aimed at identifying and preventing potential network attacks and malicious activities. Traditional malicious URL detection methods, such as Blacklist based detection and Machine learning based on feature extraction, have achieved certain results, but face challenges in real-time, accuracy, and data requirements. Therefore, this article proposes the use of Quantum Long Short-Term Memory Neural Network (QLSTM) to solve this problem. By observing the accuracy, recall, and F1 values obtained from training a large amount of data on the QLSTM network, it is shown that this method is feasible.

Algorithmically Generated Domain Names Detection Using Gated Recurrent Unit Deep Learning

The modern malware increasingly employs domain generation algorithms (DGAs) to evade traditional DNS query detection methods, such as blacklisting or reverse engineering of suspicious domain names. These algorithms generate vast numbers of random domain names to establish communication with Command and Control (C&amp;C) servers, posing significant challenges for detection. Previous research has predominantly relied on classical machine learning algorithms, necessitating manual feature extraction and classification, which is both time-consuming and labour-intensive this paper, we propose a deep learning-based architecture for detecting DGA-generated domain names. Our model utilizes recurrent networks with gated recurrent units (GRUs) for domain name detection. By converting domain names into vectors and employing GRUs, the model autonomously learns features, eliminating the need for manual intervention in feature extraction. Compared to traditional methods, our approach reduces time costs associated with feature extraction. The experimental result demonstrates the effectiveness of our proposed GRU achieving 98% accuracy, 94% recall rate, 93% precision, and an Area Under the Curve (AUC) of 99.6%. The GRUarchitecture outperforms LSTM models in terms of recall rate and accuracy while requiring less computational resources, indicating significant performance enhancement.

Dynamic detection of saturated distribution of activated carbon adsorbed volatile organic compounds based on electrical impedance tomography

The electrical impedance tomography (EIT) has been achieved for the dynamic detection of the adsorption saturation distribution in activated carbon. Volatile organic compounds (VOCs) emitted by industries pose a serious threat to human health and environmental quality. Therefore, adsorption method is widely used to treat such organic compounds. Activated carbon, as a commonly used adsorbent material, plays a crucial role in the efficient utilization and management of the adsorption process. Traditional adsorption detection methods suffer from information loss and discontinuity. We can obtain the conductivity distribution information during the current passing through the material by measuring the potential changes on the boundary of interest field, and analyze the saturation distribution information of VOCs adsorbed on activated carbon based on the conductivity distribution image. The feasibility of the technology in monitoring the saturation distribution of the adsorption process in activated carbon was verified by principle and simulation. For experimental verification, fixed bed and fluidized bed experiments were carried out, taking into account the special case of impedance change factors of activated carbon particles in the static and flow states. The experimental results confirm that in fixed-bed adsorption, the adsorption impedance change response can be effectively obtained at an excitation frequency of 1.0 MHz, while in a fluidized bed, a good adsorption impedance change response can be achieved at an excitation frequency of 4 kHz. By selecting the appropriate excitation frequency according to the change in the adsorbent’s state, stable imaging of the saturation distribution can be achieved. This study introduces a new method for visualizing the monitoring process of activated carbon adsorption.

Construction of a pH- and viscosity-switchable near-infrared fluorescent probe and its imaging application

Viscosity is a parameter used to measure the fluidity of liquids and a key indicator in evaluating the states of body fluid in biological tissues and lesions. Most traditional detection methods have many drawbacks such as a short emission wavelength and interference by background fluorescence. Inspired by the multiple double bond structure of retinal, a novel pH and viscosity dual-response fluorescent probe (Rh-TR) was constructed in this study. Rh-TR exhibited two emission signals centered at 510 and 660 nm. As the pH of the phosphate-buffered saline increased, the fluorescence at 510 nm increased by about 124-fold, while the change in fluorescence at 660 nm was not obvious. When detecting the change in viscosity using the probe, the fluorescence at 510 nm decreased by about 85 %, while the fluorescence at 660 nm increased by over 20-fold. The probe also showed high selectivity and little toxicity. As demonstrated by the biological imaging experiment, the probe successfully imaged changes in the pH and viscosity of cells and in a live animal model of zebrafish. Considering the unique structure of Rh-TR with retinal and its pH- and viscosity-switchable spectral property, the probe may find further application in detecting viscosity-related diseases and industrial detection.

Bolt loosening monitoring with passive wireless-based smart washers

Ensuring sufficient preload of bolted connections is crucial for maintaining the safety of railway operations. Accidents resulting from loose bolts caused by inadequate preload are a recurring concern. Traditional bolt preload detection methods suffer from reliability issues and bulky equipment requirements. To address these challenges, this study proposes a novel solution utilising passive wireless measurement systems. However, the communication effectiveness of these systems can be significantly impacted by the surrounding metal environment. This research introduces an intelligent clover washer design to mitigate the adverse effects of eddy currents induced by the metal environment. The design incorporates a new clover antenna and washer structure, effectively reducing the influence of the metal environment and improving the communication quality. The proposed design has undergone comprehensive prototyping, simulation and experimental verification. The results demonstrate a significant improvement over the traditional circular antenna and washer combination under similar conditions. Specifically, the sensing distance of the new clover washer is enhanced by 60% and the stable communication distance is improved by 75%. The experimental results highlight the ability of the new clover smart washer to generate a stronger spatial magnetic field and exhibit reduced susceptibility to the metal washer, thereby enhancing communication effectiveness.

Enhancing Safety and Workover Efficiency Through Real-Time Scale-Adaptive Video Detection of Critical Objects at the Wellsite

Summary The wellsite serves as the fundamental unit in the development of oil and gas fields, functioning as a hub for the production activities, with workover operations being a critical means to ensure production continuity. In addition, it plays a crucial role in environmental protection, preventing oil and gas leakage and pollution. Various pieces of mechanical equipment deployed at the wellsite are essential for tasks such as oil and gas extraction and well repair operations, holding a pivotal position in oil- and gasfield development. Consequently, intelligent wellsite implementation necessitates a primary focus on monitoring mechanical equipment, with video emerging as a vital form of multisource information at the wellsite. While existing research on wellsite video monitoring predominantly addresses system and data transmission issues, it falls short in addressing the challenges of real-time assessment and early warning in intelligent wellsite operations. This study introduces a method for identifying critical targets at the wellsite based on a scale-adaptive network. The model employs a multiscale fusion network to extract different image features and semantic features at various scales, facilitating their fusion. The processing of wellsite video images occurs in multiple stages, outputting predicted box locations and category information, enabling the localization and recognition of critical objects at the wellsite. Unlike traditional deep convolutional object detection methods, this model incorporates a parameter-free attention mechanism, enhancing the accurate feature learning of small targets during the extraction process and addressing the issue of multiscale imbalance. The experimental results validate the robust performance of the method, surpassing the latest one-stage object detection models and mainstream loss function methods. Comparative experiments demonstrate a 9.22% improvement in mean average precision (mAP) compared with YOLOv8, establishing the proposed model as a top performer in loss function optimization experiments. Furthermore, we propose a video security detection model whose results, combined with the recognition model, are applicable for video detection in wellsite scenarios. The model exhibits strong integration capabilities for scene area division and behavior regulation monitoring. In addition, the model provides valuable insights for analyzing equipment operating status, aligning with the practical needs of oil fields.

Validation of a Next Generation Sequencing Method for Adventitious Virus Detection: Demonstration of Sensitivity in Multiple Cell Lines.

In the past few years NGS has become the technology of choice to replace animal-based virus safety methods and this has been strengthened by the recent revision to the ICHQ5A virus safety chapter. Here we describe the validation of an NGS method using an agnostic analysis to detect and identify RNA virus and actively replicating DNA virus contaminants in cell banks. We report the results of the validation of each step in the sequencing process that established quality criteria to ensure consistent sequencing data. Furthermore, the validation of the analysis algorithm designed to identify virus specific sequences is described along with steps undertaken to ensure the integrity of the sequencing data from generation to analysis. Lastly, the validated sequencing and analysis systems were used to establish a limit of detection (LOD) for model viruses in cells that are commonly used in biomanufacturing. The LOD from these studies ranged from 1E+03 to 1E+04 genome copies and were dependent on the virus type with little variability between the different cell types. Thus, the validation of the NGS method for adventitious agent testing and the establishment of a general LOD for cell-based samples provides a suitable alternative to traditional virus detection methods.

A general method for trace substance detection based on cross-correlation theory

In order to improve the limit of detection (LOD) of trace substances, a general signal analysis and processing method based on cross-correlation theory is proposed, including correlation detection methods and multi-channel signal modified methods. In this paper, both methods were validated on Raman and GC-MS detection, trace substance in insulating oil (dibenzyl disulfide) is introduced as an example, and verification test of multi-substances detection is also conducted. More generally, the cross-correlation theory based method can be applied to all detection based on spectral peak areas. After applying this method, the LOD can be considerably improved. The traditional detection method based on linear fitting between characteristic peak area and trace substance concentration is implemented as a benchmark. The LOD of Raman detection in traditional way is 338.51 ppm, The LOD of GC-MS detection in traditional way is 317.26 ppb. The correlation detection results show that the LOD of Raman spectroscopy and GC-MS are respectively increased by 1.90/2.76 times compared with traditional way. And when the multi-channel signal modified method is introduced, the qualitative LOD of Raman detection reaches 9.8 ppm (number of channel is 100), the qualitative LOD of GC-MS detection reaches 39.1 ppb (number of channel is 10). Comparative study with conventional noise reduction methods have also been carried out, and the results show that correlation detection and multi-channel signal modified method are superior to them.

Rapid and real-time analysis of multi-component dissolved gas in seawater by Raman spectroscopy combined with continuous gas-liquid separator

Rapid and sensitive detection of dissolved gases in seawater is quite essential for the investigation of global carbon cycle. Large quantities of in situ optical detection techniques showed the restricted measurement efficiency, owing to the single gas sensor without the identification ability of multiple gases. In this work, a novel gas-liquid Raman detection method of monitoring the multi-component dissolved gases was proposed based on a continuous gas-liquid separator under a large difference of partial pressure. The limit of detection (LOD) of the gas Raman spectrometer could arrive at about 14 ppm for N2 gas. Moreover, based on the continuous gas-liquid separation process, the detection time of the dissolved gases could be largely decreased to about 200 s compared with that of the traditional detection method (30 min). Effect of equilibrium time on gas-liquid separation process indicated that the extracted efficiency and decay time of these dissolved gases was CO2 >O2 >N2. In addition, the analysis of the relationship between equilibrium time and flow speed indicated that the decay time decreased with the increase of the flow speed. The validation and application of the developed system presented its great potential for studying the components and spatiotemporal distribution of dissolved gases in seawater.

Target-driven cascade amplified assembly of covalent organic frameworks on tetrahedral DNA nanostructure with multiplex recognition domains for ultrasensitive detection of microRNA

BackgroundMicroRNA (miRNA) emerges as important cancer biomarker, accurate detection of miRNA plays an essential role in clinical sample analysis and disease diagnosis. However, it remains challenging to realize highly sensitive detection of low-abundance miRNA. Traditional detection methods including northern blot and real-time PCR have realized quantitative miRNA detection. However, these detection methods are involved in sophisticated operation and expensive instruments. Therefore, the development of novel sensing platform with high sensitivity and specificity for miRNA detection is urgently demanded for disease diagnosis. ResultsIn this work, a novel electrochemical biosensor was constructed for miRNA detection based on target-driven cascade amplified assembly of electroactive covalent organic frameworks (COFs) on tetrahedral DNA nanostructure with multiplex recognition domains (m-TDN). COFs were employed as nanocarriers of electroactive prussian blue (PB) molecules by the “freeze-drying-reduction” method without the use of DNA as gatekeeper, which was simple, mild and efficient. The target-triggered catalytic hairpin assembly (CHA) and glutathione reduction could convert low-abundance miRNA into a large amount of Mn2+. Without the addition of exogenous Mn2+, the dynamically-generated Mn2+-powered DNAzyme cleavage process induced abundant PB-COFs probe assembled on the four recognition domains of m-TDN, resulting in significantly signal output. Using miRNA-182-5p as the model target, the proposed electrochemical biosensor achieved ultrasensitive detection of miRNA-182-5p in the range of 10 fM-100 nM with a detection limit of 2.5 fM. Significance and NoveltyTaking advantages of PB-COFs probe as the enhanced signal labels, the integration of CHA, Mn2+-powered DNAzyme and m-TDN amplification strategy significantly improved the sensitivity and specificity of the biosensor. The designed sensing platform was capable of miRNA detection in complex samples, which provided a new idea for biomarker detection, holding promising potential in clinical diagnosis and disease screening.

Optimization of metal–organic framework nanozyme activity via histidine modification for simultaneous pesticide detection

Biosensors provide simple, rapid and sensitive detection of a wide range of analytes. Recently, they have been widely used for the rapid detection of pesticides, which makes up for the shortcomings of traditional detection methods. However, the catalytic activity of some nanozymes is not satisfactory in practical applications, limiting the performance of biosensors. Further development of highly active and stable nanozymes is conducive to improving the sensitivity of the sensors and expanding their application in practical detection. Herein we simulated the amino acid microenvironment of natural enzymes and synthesized His-MIL-101(Fe)-X with different histidine (His) modification amounts (X=0, 25%, 50%, 75%) by regulating the ratio of histidine and terephthalic acid ligands using a one-step solvothermal method. With increasing histidine doping, the peroxidase-like activity of His-MIL-101(Fe)-X was gradually enhanced, and the sensing performance of the sensor arrays constructed with the three peroxidase substrates was gradually improved, with a 25-fold increase in detection limit. Moreover, the His-MIL-101(Fe)-75% sensor array was not only able to accurately discriminate five pesticides in the range of 2-100 μM, but also to quantitatively identify different concentrations of pesticides. In addition the His-MIL-101(Fe)-75% sensor array showed good anti-interference capability and was able to discriminate five pesticides at only 2 μM in soil, lake water, seawater, and apple samples, as well as quantitatively detecting diafenthiuron, which has great potential for practical application.

Shallow mud detection algorithm for submarine channels based on improved YOLOv5s

Submarine mud poses a risk to channel navigation safety. Traditional detection methods lack efficiency and accuracy. As a result, this paper proposed an enhanced shallow submarine mud detection algorithm, leveraging an improved YOLOv5s model to increase accuracy and effectiveness in identifying such hazards in marine channels. Firstly, the sub-bottom profiler was employed to assess the submarine channel of Lianyungang Port to acquire the image data of the shallow mud sound print. Concurrently, the analysis incorporated the characteristics of changes in sound intensity peaks to precisely identify the shallow mud's location. Furthermore, the incorporation of C2F feature module into the backbone module enhances the gradient flow of the algorithm, augments the feature extraction information, and improves the algorithm's detection performance. Subsequently, Efficient Multi-Scale Attention (EMA) mechanism is incorporated into the neck module, aiming to optimize the algorithm's channel dimensions, minimize computational overhead, and enhance its detection efficiency. Finally, the study introduced Normalized Wasserstein Distance (NWD) loss function into bounding box regression loss function. This integration effectively addresses the issue of multi-scale defects, emphasizes the transformation of target planar position deviation, and improves the accuracy of the algorithm's detection capabilities. The results indicate that the improved YOLOv5s-EF algorithm outperforms the original YOLOv5s algorithm and other widely used detection algorithms. It achieved a validation set precision rate of 97.8%, recall rate of 97.6%, F1 value of 97.7%, mean Average Precision (mAP)@0.5 of 98.2%, mAP@0.95 of 69.6%, and Frames Per Second (FPS) of 51.8. YOLOv5s-EF algorithm proposed in this study offers a novel technical approach for detecting mud in submarine channels, which is importance for ensuring the safe operation and maintenance of dredging in such channels.

Enhancing Object Detection Accuracy Through Custom Dataset Using Yolo

Abstract: Potholes pose significant risks to road safety and vehicle maintenance, leading to accidents and costly repairs. Traditional methods of pothole detection are often labour-intensive and time-consuming. In this study, we propose an innovative approach to pothole detection using YOLOv8, a state-of-the-art object detection algorithm. By harnessing the power of deep learning, our system can accurately identify and locate potholes in real-time video streams from traffic cameras and vehicles. We employ YOLOv8, an advanced variant of the You Only Look Once (YOLO) algorithm, known for its speed and accuracy in real-time object detection tasks. Leveraging a large annotated dataset of road images, we fine-tune the YOLOv8 model to specifically detect potholes. Our trained model is capable of identifying various pothole sizes and shapes, even in challenging lighting and weather conditions.