Detection Efficiency Research Articles

Multi-class Object Detection in Urban Scenes Based on Deep Learning

Abstract. The rapid development of urbanization presents challenges and requirements for multi-class object detection in urban scenes. Accurately identifying buildings, vehicles, and trees in urban scenes can optimize urban planning, traffic management, monitoring environmental conditions, and promote the development of smart cities. Traditional target detection methods perform poorly in complex urban environments, while deep learning technology achieves accurate target recognition and positioning by automatically extracting high-level semantic features. In this study, we chose to use the YOLOv5s algorithm for multi-class target detection in urban scenes. YOLOv5s is a lightweight deep learning model with small storage space and efficient detection speed. In this paper, the Potsdam area data published by ISPRS is used to make the label data of buildings, vehicles and trees. The YOLOv5s algorithm is used to iteratively train the model. The results show that the mAP value detected by the YOLOv5s model can reach 82.83%. The experimental results show that the algorithm shows higher accuracy than SSD and Faster R-CNN in tree detection. Although it has a slight decline in building and vehicle detection, considering the factors such as detection accuracy, speed, and model size, the YOLOv5s algorithm has a better recognition and detection effect for the detection of multi-class targets in urban scenes.

Nanoarchitectonics with cetrimonium bromide on metal nanoparticles for linker-free detection of toxic metal ions and catalytic degradation of 4-nitrophenol.

Heavy metal ions and organic pollutants, such as 4-nitrophenol (4-NP), pose significant environmental and human health threats. Addressing these challenges necessitates using advanced nanoparticle-based systems capable of efficient detection and degradation. However, conventional approaches utilizing strong capping agents like cetrimonium bromide (CTAB) on nanoparticles lead to limitations due to the rigid nature of CTAB. This restricts its utility in heavy metal detection and 4-NP degradation, requiring additional surface modifications using linker molecules, thereby increasing process complexity and cost. To overcome these limitations, there is a critical need for the development of an easy-to-use, dual-functional, linker-free nanosystem capable of simultaneous detection of heavy metals and efficient degradation of 4-NP. For enabling linker-free/ligand-free detection of heavy metal ions and catalytic degradation of 4-NP, CTAB was engineered as a versatile capping agent on gold and silver nanoparticles. Various factors, including nanoparticle characteristics such as shape, size, metal composition, centrifugation, and NaOH amount, were investigated for their impact on the performance of CTAB-capped nanoparticles in heavy metal detection and 4-NP degradation. CTAB-Au nanospheres demonstrated limited heavy metal ion detection capability but exhibited remarkable efficiency in degrading 94.37% of 4-NP within 1 min. In contrast, silver nanospheres effectively detected Hg2+, Cu2+, and Fe3+ at concentrations as low as 1 ppm and degraded 90.78% of 4-NP within 30 min. Moreover, anisotropic gold nanorods (CTAB-AuNR1 and CTAB-AuNR2) showed promising sensing capabilities towards Cu2+, Cr3+, and Hg2+ at 0.5 OD, while efficiently degrading 4-NP within 5 min at 1 OD. This study emphasizes the importance of tailoring parameters of CTAB-capped nanoparticles for specific sensing and catalytic applications, offering potential solutions for environmental remediation and human health protection.

Precision Agriculture Technologies for Early Detection of Crop Pests and Diseases

Crop pests and diseases pose significant challenges to agricultural productivity and food security worldwide. Traditional methods for detecting and managing these threats often rely on manual scouting and blanket pesticide applications, which can be labor-intensive, time-consuming, and environmentally harmful. Precision agriculture technologies offer promising solutions for early detection and targeted management of crop pests and diseases. This review article provides a comprehensive overview of the latest precision agriculture tools and techniques for monitoring crop health, detecting pests and diseases, and guiding site-specific interventions. Key technologies discussed include remote sensing, proximal sensing, machine learning, robotics, and Internet of Things (IoT) sensors. The article highlights the potential of these technologies to improve the timeliness, accuracy, and efficiency of pest and disease detection while reducing reliance on chemical inputs. It also discusses the challenges and opportunities for integrating these technologies into current agricultural practices and extension services. The review concludes with recommendations for future research and development to advance precision agriculture solutions for sustainable crop protection.

IoT Intrusion Detection Model Using RNN Algorithm

An Intrusion Detection System (IDS) is the method that is processed in the network layer of an IoT system. Machine learning techniques have been applied in the IDS and observed the higher performance in identifying the intrusion and malware. The existing method involved in IDS tends to be ineffective due to the drawbacks of big data, centralization, and low privacy. The existing method is also inefficient in handling the streaming data of IoT systems. Most of the method in the IDS has low efficiency in intrusion detection to increase the efficiency of the detection. In this research, the FPA is proposed in IoT intrusion detection to increase the efficiency of the detection.

Efficient Small Object Detection You Only Look Once: A Small Object Detection Algorithm for Aerial Images.

Aerial images have distinct characteristics, such as varying target scales, complex backgrounds, severe occlusion, small targets, and dense distribution. As a result, object detection in aerial images faces challenges like difficulty in extracting small target information and poor integration of spatial and semantic data. Moreover, existing object detection algorithms have a large number of parameters, posing a challenge for deployment on drones with limited hardware resources. We propose an efficient small-object YOLO detection model (ESOD-YOLO) based on YOLOv8n for Unmanned Aerial Vehicle (UAV) object detection. Firstly, we propose that the Reparameterized Multi-scale Inverted Blocks (RepNIBMS) module is implemented to replace the C2f module of the Yolov8n backbone extraction network to enhance the information extraction capability of small objects. Secondly, a cross-level multi-scale feature fusion structure, wave feature pyramid network (WFPN), is designed to enhance the model's capacity to integrate spatial and semantic information. Meanwhile, a small-object detection head is incorporated to augment the model's ability to identify small objects. Finally, a tri-focal loss function is proposed to address the issue of imbalanced samples in aerial images in a straightforward and effective manner. In the VisDrone2019 test set, when the input size is uniformly 640 × 640 pixels, the parameters of ESOD-YOLO are 4.46 M, and the average mean accuracy of detection reaches 29.3%, which is 3.6% higher than the baseline method YOLOv8n. Compared with other detection methods, it also achieves higher detection accuracy with lower parameters.

Efficient Flood Detection through Hybrid Machine Learning and Metaheuristic Methods using Sentinel-1

Abstract. Floods are considered among the most destructive natural disasters, requiring precise and timely management. Remote sensing, utilizing diverse satellite imagery data, enables effective monitoring and assessment of flood impacts. In this context, machine learning and deep learning methods, as effective and scalable approaches, can significantly enhance the accuracy of flood detection and management by analyzing remote sensing data, thereby playing a crucial role in mitigating flood-related risks. In this study, to flood detection using Sentinel-1 SAR data, machine learning algorithms, including Random Forest (RF) and Histogram-based Gradient Boosting Decision Tree, were employed, along with two metaheuristic algorithms, Harris Hawks Optimization (HHO) and Ant Colony Optimization (ACO), for hyperparameter optimization. Additionally, to enhance the models' ability to detect flooded pixels and improve overall performance accurately, a pre-trained VGG-16 Neural Network was used as a deep feature extractor. Finally, four ensemble flood detection models—RF-HHO, RF-ACO, HGBDT-HHO, and HGBDT-ACO—were implemented, and their performance was evaluated and compared based on statistical metrics. Based on the obtained results, all four ensemble flood detection models demonstrated excellent performance in the validation and testing phases. The overall accuracy of these models reached over 95% in the validation phase and exceeded 97% in the testing phase. However, the HGBDT-ACO model achieved the highest accuracy and the lowest error rate in detecting flood pixels, making it the best-performing model in this study. Generally, HGBDT models showed a relative advantage over RF models, as they required significantly less time for training while achieving comparable results. Therefore, they were efficient and performed better in terms of computational complexity.

A Novel Landmark-based Semi-supervised Deep Learning Method for Cerebral Aneurysm Detection Using TOF-MRA

Background: Time-of-flight (TOF) magnetic resonance angiography (MRA) is widely used to identify aneurysm in human brain. Various deep learning models have been developed to help TOF-MRA reading in the field. The performance of those TOF-MRA analysis tools, however, faces several limitations in cerebral aneurysm detection. These challenges primarily come from the fact that cerebral aneurysms occupy less than 0.1% of the total TOF-MRA voxel size. This study aims to improve the efficiency of cerebral aneurysm detection by developing a landmark-based semi-supervised deep learning method, a technology that automatically generates landmark boxes in areas with a high probability of cerebral aneurysm occurrence.Methods: We used data from a total of 500 aneurysm-positive and 50 aneurysm-negative subjects. The aneurysm detection model was developed using clustering and a dilated residual network.Results: When the number of landmarks was ten and their size was 36 mm<sup>3</sup>, the best performance was achieved in our experiment. Although landmark occupies a small portion of the entire image, up to 98.2% of landmarks were cerebral aneurysms. The sensitivity of the model for cerebral aneurysm detection was 83.0%, with a false positive rate of 3.4%.Conclusions: This study developed a deep learning model using TOF-MRA image. This model generates the most suitable landmarks for each individual, excluding unnecessary areas for cerebral aneurysm detection, which makes it possible to focus on areas with a high probability of occurrence. This model is expected to enhance the efficiency and accuracy of cerebral aneurysm detection in the field.

Comparison of quench correction methods for 90Sr/90Y assessment in waters after nuclear incident

One important matter of environmental radioactivity monitoring is the accuracy and precision of the 90Sr/90Y measurement techniques. When Cherenkov counting is carried out in coloured waters, the reduction in detection efficiency can cause significant errors in the obtained results. Two colour quench correction methods were considered in this study: the conventional SCR (Sample Channels Ratio) technique and the Muonic peak method. The latter indicates the quench level via the position of the muonic peak in the cosmic background spectrum. So far, there have been no reports about the Muonic peak method’s implementation in Cherenkov counting of 90Sr/90Y in waters. The performance, limitations, advantages, and drawbacks of two methods have been compared based on the analysis of coloured spiked test samples. The Muonic peak method provided similar accuracy as the SCR method in test samples. The adequacy and effectiveness of both techniques have been confirmed during 90Sr routine monitoring in the event of a nuclear emergency or radioactive leakage from nuclear facilities.

Time performance of Analog Pixel Test Structures with in-chip operational amplifier implemented in 65 nm CMOS imaging process

In the context of the CERN EP R&D on monolithic sensors and the ALICE ITS3 upgrade, the Tower Partners Semiconductor Co (TPSCo) 65nm process has been qualified for use in high energy physics, and adopted for the ALICE ITS3 upgrade. An Analog Pixel Test Structure (APTS) featuring fast per pixel operational-amplifier-based buffering for a small matrix of four by four pixels, with a sensor with a small collection electrode and a very non-uniform electric field, was designed to allow detailed characterization of the pixel performance in this technology. Several variants of this chip with different pixel designs have been characterized with a 120GeV/c positive hadron beam. This result indicates that the APTS-OA prototype variants with the best performance achieve a time resolution of 63ps with a detection efficiency exceeding 99% and a spatial resolution of 2μm, highlighting the potential of TPSCo 65 nm CMOS imaging technology for high-energy physics and other fields requiring precise time measurement, high detection efficiency, and excellent spatial resolution.

A cosmic ray muons telescope based on bar plastic scintillator detectors

A cosmic ray muons telescope (CORMT) was designed to monitor the distribution of cosmic ray muons in the environment. CORMT is made of three main components: the mechanical structure, the detector, and the readout electronics. The mechanical structure consists of a support structure and a rotating moving platform that ensures the stability and accuracy of the test. The detector consists of four bar-shaped plastic scintillators each coupled with a silicon photomultiplier (SiPM). The readout electronics design uses Xilinx Artix 7 series field-programmable gate array (FPGA), combined with peripheral circuit design, to complete the readout of detector cosmic ray muons hit information. CORMT has the advantage of adjustable thresholds, real-time output, and the use of Coordinated Universal Time (UTC) to record events. The CORMT test results show that the detection efficiency can reach over 90%, the accidental compliance count rate is around 0.02, and the muon flux conforms to the distribution law. This article will discuss the design and performance of the CORMT.

Eu(III) encapsulation strategy to construct intelligent ratiometric luminescent sensor for efficient detection of phenylglyoxylic acid biomarker in bodily fluids

Herein, a desirable MOF-based luminescent sensor of Eu3+@ZnMOF is fabricated through post-synthesis strategy. Based on response differences of the inherent monitoring signal at 442 nm and the secondary monitoring signal at 699 nm introduced by encapsulating Eu3+ ions, the prepared Eu3+@ZnMOF displays great superior as a ratiomertic luminescent sensor with high sensitive and selective, rapid response time (less than 1 min), superior detection limit (3.89 ng·mL−1), and favorable recyclable performance (at least 6 cycles) in detecting phenylglyoxylic acid (PGA) biomarker over other urine and serum species. Furthermore, Eu3+@ZnMOF also demonstrates exceptional detectability and remarkable reliability in tracing PGA levels within actual serum and urine samples. On the basis of PGA levels controlled luminescence changes of Eu3+@ZnMOF under UV light, an intelligent detection platform is developed by integrating tandem combinational “AND” logic gates to achieve more convenient and intuitive detection of PGA levels in body fluids. Besides, photoinduced electron transfer and internal filtering effect are demonstrated to be responsible for the luminescence sensing. This work not only offers a validated strategy for the design of MOFs-based ratiometric luminescent sensors, but also presents an approach in efficient, rapid, and intelligent detection of biomarkers in body fluids.

Deep learning method to automatically diagnose periodontal bone loss and periodontitis stage in dental panoramic radiograph.

Artificial intelligence (AI) could be used as an automatically diagnosis method for dental disease due to its accuracy and efficiency. This research proposed a novel convolutional neural network (CNN)-based deep learning (DL) ensemble model for tooth position detection, tooth outline segmentation, tooth tissue segmentation, periodontal bone loss and periodontitis stage prediction using dental panoramic radiographs. The dental panoramic radiographs of 320 patients during the period January 2020 to December 2023 were collected in our dataset. All images were de-identified without private information. In total, 8462 teeth were included. The algorithms that DL ensemble model adopted include YOLOv8, Mask R-CNN, and TransUNet. The prediction results of DL method were compared with diagnosis of periodontists. The periodontal bone loss degree deviation between the DL method and ground truth drawn by the three periodontists was 5.28%. The overall PCC value of the DL method and the periodontists' diagnoses was 0.832 (P <​ 0.001). ​The ICC value was 0.806 (P <​ 0.001). The total diagnostic accuracy of the DL method was 89.45%. The proposed DL ensemble model in this study shows high accuracy and efficiency in radiographic detection and a valuable adjunct to periodontal diagnosis. The method has strong potential to enhance clinical professional performance and build more efficient dental health services. The DL method not only could help dentists for rapid and accurate auxiliary diagnosis and prevent medical negligence, but also could be used as a useful learning resource for inexperienced dentists and dental students.

XSShield: A Novel Dataset and Lightweight Hybrid Deep Learning Model for XSS Attack Detection

With the proliferation of web applications, cross-site scripting (XSS) attacks have increased significantly and now pose a significant threat to users' information security and privacy. To enhance the efficiency of XSS attack detection, the adoption of machine learning (ML) and deep learning (DL) techniques offers promising solutions, but their effectiveness is limited by the lack of comprehensive and diverse datasets. Moreover, existing approaches often prioritize detection accuracy over real-time processing capabilities, which are essential for effective defense. To address these challenges, in this paper, we propose a novel framework that automatically collects web resources, efficiently extracts informative features, and constructs an up-to-date XSS attack dataset, which is then used to train a machine learning-based XSS detection model. Using this framework, we created and published a well-structured dataset over 100,000 samples for the research community. Furthermore, we present a hybrid detection model that leverages the strengths of both Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. Extensive evaluations of our dataset demonstrate that the proposed model outperforms other baseline ML models across various metrics, including processing rate. Notably, our model achieves an accuracy of 99.27% while maintaining a low false positive rate of 0.06% and high processing rate of exceeding 1000 samples per second. These results highlight its high accuracy and robustness in detecting XSS, and suitability for real-time applications. Our work presents a comprehensive solution for enhancing web application security by providing a diverse dataset and a high-accuracy detection model with low latency.

Development of a cutting-edge ensemble pipeline for rapid and accurate diagnosis of plant leaf diseases

Selecting techniques is a crucial aspect of disease detection analysis, particularly in the convergence of computer vision and agricultural technology. Maintaining crop disease detection in a timely and accurate manner is essential to maintaining global food security. Deep learning is a viable answer to meet this need. To proceed with this study, we have developed and evaluated a disease detection model using a novel ensemble technique. We propose to introduce DenseNetMini, a smaller version of DenseNet. We propose combining DenseNetMini with a learning resizer in ensemble approach to enhance training accuracy and expedite learning. Another unique proposition involves utilizing Gradient Product (GP) as an optimization technique, effectively reducing the training time and improving the model performance. Examining images at different magnifications reveals noteworthy diagnostic agreement and accuracy improvements. Test accuracy rates of 99.65 %, 98.96 %, and 98.11 % are seen in the Plantvillage, Tomato leaf, and Appleleaf9 datasets, respectively. One of the research's main achievements is the significant decrease in processing time, which suggests that using the GP could improve disease detection in agriculture's accessibility and efficiency. Beyond quantitative successes, the study highlights Explainable Artificial Intelligence (XAI) methods, which are essential to improving the disease detection model's interpretability and transparency. XAI enhances the interpretability of the model by visually identifying critical areas on plant leaves for disease identification, which promotes confidence and understanding of the model's functionality.

Multi-crosslinked Ta4C3TX MXene composites with “interlocked structure” for efficient gamma-ray shielding, behavioural detection and thermal management in nuclear environments

Multi-crosslinked Ta4C3TX MXene composites with “interlocked structure” for efficient gamma-ray shielding, behavioural detection and thermal management in nuclear environments

Enhanced chemiluminescence with sub-1 nanometer CuO-PMA nanosheets for the sensitive detection of quercetin

BackgroundChemiluminescence (CL) analysis, characterized by its simple instrumentation, high signal-to-noise ratio, wide linear range, and minimal background interference, has garnered increasing attention from researchers. Nanomaterials (NMs) have been explored to enhance CL intensity. Notably, sub-1 nanometer scale NMs are considered to hold significant untapped potential due to their size effects. The application of these sub-1 nanometer NMs in enhancing CL is anticipated to yield favorable results. Additionally, the low water solubility and bioavailability of quercetin glycosides lead to their presence in bodily fluids at only trace levels, highlighting the urgent need for efficient and rapid detection methods. ResultsIn this work, phosphomolybdic acid (PMA) was incorporated into CuO to synthesize sub-1 nanometer CuO-PMA nanosheets (SNSs) using a cluster-core co-assembly strategy. Conformational and structural characterization confirmed the successful synthesis of these nanosheets. The CuO-PMA SNSs were employed to enhance the CL emission of the luminol-H2O2 system, resulting in an increase of over 1000 times. The catalytic properties of CuO-PMA SNSs significantly facilitated the decomposition of H2O2, leading to an enhanced production of reactive oxygen species, which in turn induced the CL enhancement. Given that the antioxidant effect of quercetin would consume the reactive oxygen species generated during the catalysis, a decrease in CL intensity was anticipated. A CL sensor for quercetin detection was developed based on the CuO-PMA SNSs-luminol-H2O2 system, demonstrating a strong linear relationship (R2 = 0.9969) and a low detection limit of 0.31 nM. SignificanceThis research provides a strategy to enhance the CL intensity of the luminol-H2O2 system by using CuO-PMA SNSs, offering a highly sensitive assay for detecting quercetin concentrations. The method is characterized as a simple and cost-effective analytical strategy making CL analysis very attractive for chemical analysts.

Efficient low-temperature detection of CO gas by various metalated porphyrinic-Al-based MOF (Cu and Co) materials

A series of porphyrinic-Al-based metal–organic framework (MOF) nanomaterials were successfully fabricated via a simple solvothermal procedure and denoted as AlPMOF(M) (M = Cu, Co). The materials exhibited high surface areas with rectangular morphologies and the desired phases. Chemical composition studies demonstrated the presence of Cu and Co ions in the synthesized samples, while thermal studies showed the samples were stable up to 400 °C. CO gas-sensing studies revealed better CO sensing performances of both the AlPMOF(Cu) and AlPMOF(Co) gas sensors compared to the pristine sensor at room temperature. AlPMOF sensor showed responses of 1.06 and 1.05 to 10 ppm CO gas at 150 and 25ºC, respectively, with no selectivity to CO gas at 25ºC. AlPMOF (Co) sensor revealed responses of 1.11 and 1.06 to 10 ppm CO gas at 200 and 25ºC, respectively, with selectivity to CO gas at 25ºC. Also, AlPMOF (Cu) sensor revealed responses of 1.12 and 1.07 to 10 ppm CO gas at 150 and 25ºC, respectively, with selectivity to CO gas at 25ºC. In addition, the sensing mechanism and enhanced response to CO gas were elucidated. These findings demonstrate the capability of these novel sensing materials for CO gas sensing at low temperatures.

Internal defect detection model based on laser ultrasonic signal decomposition and deep learning

It is essential to accurately detect the internal defects of metal equipment. The accuracy of traditional detection methods is affected by noise. This paper proposes an innovative genetic algorithm-weighted permutation entropy (GA-WPE) optimized Variational mode Decomposition (VMD), continuous wavelet Transform (CWT) and deep learning fusion model, which aims to achieve efficient automatic defect detection. Firstly, the laser ultrasonic experimental system was built, and the hole defects with different diameters and depths were prepared. The GA-WPE-VMD was used to decompose and reconstruct the ultrasound signal, and CWT converted the reconstructed signal into a time–frequency scalogram. Three deep learning models were used for defect detection. ViT model achieved 99.88%, 97.01%, and 91.7% accuracy on the training, validation, and test set, respectively, significantly better than DenseNet121 and ResNet50. The AUC of ViT on the test set was close to 100%. The results verify the feasibility and effectiveness of the proposed method.

Fighting against forest fire: A lightweight real-time detection approach for forest fire based on synthetic images

Forest fires are known for their high level of randomness and unpredictability, which often lead to significant ecological damage and human life loss. Existing forest fire detection technologies are not capable of detecting small-scale flames or smoke in real time, thus failing to meet the demands of real-time detection of forest fires using Unmanned Aerial Vehicles (UAVs). To overcome these limitations, we propose an efficient and lightweight forest fire detection method that utilizes synthetic images and UAVs to achieve real-time and high-precision detection of forest fires against complex backgrounds. Firstly, we propose the Dilation Repconv Cross Stage Partial Network (DRCSPNet), which enhances the detection capabilities for multiscale flames and smoke using multi-branch parallel joint dilation convolution and batch normalization, while effectively extracting features from different stages of forest fires. Secondly, to mitigate challenges associated with extreme lighting in forest scenes and large contrast variation in fire images, we propose a Global Mixed-Attention (GMA) model across feature pyramids to enhance information lost in high-dimensional feature maps and increase the robustness of the model through a multiscale fusion strategy. Finally, we present the Lite-Path Aggregation Network (Lite-PAN) with varying scales to improve effective feature flow for multilevel forest fires, addressing challenges that arise from various climatic conditions. Furthermore, we employ Unreal Engine 5 to generate forest fire datasets in four scenarios to address the issue of relatively limited aerial forest fire datasets. According to the results of the experiment, our proposed method achieves 58.39% mAP(mean Average Precision) with 5.703 GFLOPs (Giga Floating Point Operations Per Second) while yielding a frame rate of 33.5 Frames Per Second (FPS) on NVIDIA Jetson NX. Extensive experiment results demonstrate our method has the advantage of being in real time, extremely accurate, and easily implementable compared to state-of-the-art techniques.

Leveraging AI to Revolutionize Subscription Business Models

This article explores the transformative impact of Artificial Intelligence (AI) on subscription-based business models across various industries. It examines how AI is revolutionizing key aspects of subscription services, including personalization, customer retention, pricing strategies, customer support, operational efficiency, and fraud detection. The article highlights specific AI applications such as content recommendations, dynamic user interfaces, churn prediction, advanced customer segmentation, dynamic and usage-based pricing, AI-powered chatbots, and sentiment analysis. Additionally, it discusses how AI enhances operational efficiency through automated billing and inventory management, and improves security via anomaly detection and behavioral biometrics. Case studies of Adobe Creative Cloud and Amazon Web Services (AWS) are presented to illustrate real-world applications of AI in subscription services. The article concludes by emphasizing the paradigm shift AI represents in customer engagement, operational optimization, and revenue generation for subscription businesses, forecasting continued innovation and opportunities in this rapidly evolving sector.