Low Detection Efficiency Research Articles

Surface Defect Identification of Strip Steel Using ViT‐RepVGG

In the production of strip steel, surface defect identification is crucial for improving product quality and ensuring smooth subsequent processes. Existing technologies face challenges such as low detection efficiency and susceptibility to environmental noise. This article employs an automated deep learning method without requiring consideration of complex environmental changes and proposes an improved RepVGG (ViT‐RepVGG) model for surface defect identification. The model is based on the RepVGG architecture, and the study explores the impact of incorporating the self‐attention mechanism of ViT under various addition strategies on model performance. A comparison is made between the optimized model and classic network models, as well as recently published models, in terms of identification performance. The research also examines the performance variations of the model under different hyperparameter settings and its identification performance for six types of defects. The results indicate that adding the ViT module to stage 3 of the A1‐type RepVGG, with a learning rate, optimizer, and activation function set to 0.0001, Adam, and Gelu, respectively, yields the optimal ViT‐RepVGG model performance. These findings demonstrate the feasibility of enhancing classification performance by incorporating the self‐attention mechanism into neural networks, providing an effective foundation for the online identification of strip steel surface defects.

Loss tolerance quantum key distribution based on the signal extraction model and advantage distillation technology

Compared with traditional networks, quantum key distribution (QKD) offers the ultimate resources, allowing two remote users to share secret symmetric keys regardless of the capabilities of eavesdroppers. However, the widespread application of commercial QKD is still challenging due to the low photon detection efficiency and the extremely high transmission loss. Here we demonstrate a fully commercial phase-encoding QKD system using a signal extraction model and advantage distillation technology to suppress detector noise and perform real-time pre-error correction. 1.89 × 10−10 in the asymptotic case and 7.43 × 10−12 in the nonasymptotic case secret key bits per pulse are achieved with a total loss of 70.05 dB. This method not only increases the transmission loss tolerance but also provides a more realistic deployment of quantum communication.

Detecting command injection attacks in web applications based on novel deep learning methods

Web command injection attacks pose significant security threats to web applications, leading to potential server information leakage or severe server disruption. Traditional detection methods struggle with the increasing complexity and obfuscation of these attacks, resulting in poor identification of malicious code, complicated feature extraction processes, and low detection efficiency. To address these challenges, a novel detection model, the Convolutional Channel-BiLSTM Attention (CCBA) model, is proposed, leveraging deep learning techniques to enhance the identification of web command injection attacks. The model utilizes dual CNN convolutional channels for comprehensive feature extraction and employs a BiLSTM network for bidirectional recognition of temporal features. An attention mechanism is also incorporated to assign weights to critical features, improving the model’s detection performance. Experimental results demonstrate that the CCBA model achieves 99.3% accuracy and 98.2% recall on a real-world dataset. To validate the robustness and generalization of the model, tests were conducted on two widely recognized public cybersecurity datasets, consistently achieving over 98% accuracy. Compared to existing methods, the proposed model offers a more effective solution for identifying web command injection attacks.

Study on Fractal Damage of Concrete Cracks Based on U-Net

The damage degree of a reinforced concrete structure is closely related to the generation and expansion of cracks. However, the traditional damage assessment methods of reinforced concrete structures have defects, including low efficiency of crack detection, low accuracy of crack extraction, and dependence on the experience of inspectors to evaluate the damage of structures. Because of the above problems, this paper proposes a damage assessment method for concrete members combining the U-Net convolutional neural network and crack fractal features. Firstly, the collected test crack images are input into U-Net for segmenting and extracting the output cracks. The damage to the concrete structure is then classified into four empirical levels according to the damage index (DI). Subsequently, a linear regression equation is constructed between the fractal dimension (D) of the cracks and the damage index (DI) of the reinforced concrete members. The damage assessment is then performed by predicting the damage index using linear regression. The method was subsequently employed to predict the damage level of a reinforced concrete shear wall–beam combination specimen, which was then compared with the actual damage level. The results demonstrate that the damage assessment method for concrete members proposed in this study is capable of effectively identifying the damage degree of the concrete members, indicating that the method is both robust and generalizable.

New plant health monitoring framework

With the continuous advancement of technology and the expanding demands of the agricultural market, plant health monitoring holds significant importance in the agricultural sector. However, according to statistics, most current monitoring models exhibit low detection efficiency and inadequate accuracy. To address these issues and specifically enhance the efficiency and accuracy of plant health monitoring, this paper proposes a recognition strategy and plan composed of two parts: (1) identification of plant species and (2) health monitoring, diagnosis, and treatment recommendations. During the health monitoring process, issues such as plant diseases, pests, nutrient deficiencies, and inadequate environmental conditions can be detected. This strategy, implemented through a systematic automated monitoring system, will greatly benefit the large-scale development of agriculture.

Detection of Camellia oleifera fruit maturity in orchards based on modified lightweight YOLO

Fruit maturity is the main factor affecting the quality and yield of Camellia oil. At present, accurate and efficient detection of the maturity level of Camellia oleifera fruits in orchards and enabling selective picking for harvesting robots is a crucial issue. Aiming at the challenge of low detection efficiency of Camellia oleifera fruit maturity and complex object detection models that are difficult to deploy to Camellia oleifera selective harvesting robots, a modified lightweight You Only Look Once model (YOLO-LM) based on YOLOv7-tiny is proposed. Specifically, three Criss-Cross Attention (CCA) modules are introduced to the backbone network to reduce the influence of leaves and branches occlusion as well as mutual occlusion between fruits. Then, the Adaptively Spatial Feature Fusion (ASFF) module is applied as the head to solve the limitation of inconsistency across different feature scales on the feature pyramid. Furthermore, the GSConv is introduced to replace the standard convolution of the Neck network to enable the model to simultaneously preserve model accuracy and reduce model complexity. Experiment results demonstrate that the precision, recall, mAP@0.5, parameters, FLOPs, and model size of YOLO-LM reached 93.96 %, 93.32 %, 93.18 %, 10.17 million, 19.46 G, and 19.82 MB, which outperformed YOLOv3, Faster R-CNN (VGG16), Faster R-CNN (ResNet50), YOLOv4, and YOLOv5m. Compared to YOLOv3-tiny, YOLOv4-tiny, YOLOv5s, YOLOv7-tiny, and YOLOv8s, the mAP@0.5 of YOLO-LM increased by 6.24 %, 6.11 %, 2.52 %, 2.45 %, and 0.57 %, respectively. Meanwhile, the parameters and model size of YOLO-LM are slightly higher than that of YOLOv3-tiny, YOLOv4-tiny, YOLOv5s and YOLOv7-tiny, and significantly lower than that of YOLOv8s. In addition, the detection heatmaps of YOLOv7-tiny and YOLO-LM demonstrate these optimization operations contribute to the enhanced performance of maturity detection for Camellia oleifera fruits within natural orchard environments. Overall, the YOLO-LM model can be used for the maturity detection of Camellia oleifera fruits in orchards, which is expected to provide theoretical reference for orchard yield estimation, growth monitoring, planting management optimization and the development of Camellia oleifera selective harvesting robots.

Reflective Distributed Denial of Service Detection: A Novel Model Utilizing Binary Particle Swarm Optimization-Simulated Annealing for Feature Selection and Gray Wolf Optimization-Optimized LightGBM Algorithm.

The fast growth of the Internet has made network security problems more noticeable, so intrusion detection systems (IDSs) have become a crucial tool for maintaining network security. IDSs guarantee the normal operation of the network by tracking network traffic and spotting possible assaults, thereby safeguarding data security. However, traditional intrusion detection methods encounter several issues such as low detection efficiency and prolonged detection time when dealing with massive and high-dimensional data. Therefore, feature selection (FS) is particularly important in IDSs. By selecting the most representative features, it can not only improve the detection accuracy but also significantly reduce the computational complexity and attack detection time. This work proposes a new FS approach, BPSO-SA, that is based on the Binary Particle Swarm Optimization (BPSO) and Simulated Annealing (SA) algorithms. It combines these with the Gray Wolf Optimization (GWO) algorithm to optimize the LightGBM model, thereby building a new type of reflective Distributed Denial of Service (DDoS) attack detection model. The BPSO-SA algorithm enhances the global search capability of Particle Swarm Optimization (PSO) using the SA mechanism and effectively screens out the optimal feature subset; the GWO algorithm optimizes the hyperparameters of LightGBM by simulating the group hunting behavior of gray wolves to enhance the detection performance of the model. While showing great resilience and generalizing power, the experimental results show that the proposed reflective DDoS attack detection model surpasses conventional methods in terms of detection accuracy, precision, recall, F1-score, and prediction time.

Universal strategy for rapid design and analysis of gas detection peptide chips with positional preference

The design and analysis of gas detection chips directly affect their detection efficiency and applicability. Detection devices are currently restricted by detection principles, facing drawbacks like intricate structural design, limited applicability, and low detection efficiency. We have designed a complete set of design and analysis scheme for a peptide gas detection chip. First, we selected specific and high-affinity peptide combinations from existing peptide-gas affinity datasets. Then, the peptide chip's arrangement was grouped according to the variations in peptides' affinity towards different gases. Peptides were arranged based on their affinity levels within each group, striking a balance between discrimination and flexibility in the design of the chip. Finally, we evaluated the analysis methods by generating simulated data based on a reference affinity matrix constructed from actual data. Due to the preprocessing role of chip design on affinity data, all methods can effectively accomplish gas classification. In gas concentration prediction tasks, our method reduced mean square error to 0.41, significantly outperforming other methods. This gas detection scheme shortens the development cycle of chip design and analysis methods, fully utilizing the specificity of peptides, enhancing gas analysis effectiveness, and demonstrating the agile development of gas detection chips.

Entropy Bounds for Device-Independent Quantum Key Distribution with Local Bell Test.

One of the main challenges in device-independent quantum key distribution (DIQKD) is achieving the required Bell violation over long distances, as the channel losses result in low overall detection efficiencies. Recent works have explored the concept of certifying nonlocal correlations over extended distances through the use of a local Bell test. Here, an additional quantum device is placed in close proximity to one party, using short-distance correlations to verify nonlocal behavior at long distances. However, existing works have either not resolved the question of DIQKD security against active attackers in this setup, or used methods that do not yield tight bounds on the key rates. In this work, we introduce a general formulation of the key rate computation task in this setup that can be combined with recently developed methods for analyzing standard DIQKD. Using this method, we show that if the short-distance devices exhibit sufficiently high detection efficiencies, positive key rates can be achieved in the long-distance branch with lower detection efficiencies as compared to standard DIQKD setups. This highlights the potential for improved performance of DIQKD over extended distances in scenarios where short-distance correlations are leveraged to validate quantum correlations.

LD-YOLO: A Lightweight Dynamic Forest Fire and Smoke Detection Model with Dysample and Spatial Context Awareness Module

The threat of forest fires to human life and property causes significant damage to human society. Early signs, such as small fires and smoke, are often difficult to detect. As a consequence, early detection of smoke and fires is crucial. Traditional forest fire detection models have shortcomings, including low detection accuracy and efficiency. The YOLOv8 model exhibits robust capabilities in detecting forest fires and smoke. However, it struggles to balance accuracy, model complexity, and detection speed. This paper proposes LD-YOLO, a lightweight dynamic model based on the YOLOv8, to detect forest fires and smoke. Firstly, GhostConv is introduced to generate more smoke feature maps in forest fires through low-cost linear transformations, while maintaining high accuracy and reducing model parameters. Secondly, we propose C2f-Ghost-DynamicConv as an effective tool for increasing feature extraction and representing smoke from forest fires. This method aims to optimize the use of computing resources. Thirdly, we introduce DySample to address the loss of fine-grained detail in initial forest fire images. A point-based sampling method is utilized to enhance the resolution of small-target fire images without imposing an additional computational burden. Fourthly, the Spatial Context Awareness Module (SCAM) is introduced to address insufficient feature representation and background interference. Also, a lightweight self-attention detection head (SADH) is designed to capture global forest fire and smoke features. Lastly, Shape-IoU, which emphasizes the importance of boundaries’ shape and scale, is used to improve smoke detection in forest fires. The experimental results show that LD-YOLO realizes an mAP0.5 of 86.3% on a custom forest fire dataset, which is 4.2% better than the original model, with 36.79% fewer parameters, 48.24% lower FLOPs, and 15.99% higher FPS. Therefore, LD-YOLO indicates forest fires and smoke with high accuracy, fast detection speed, and a low model complexity. This is crucial to the timely detection of forest fires.

A Global Feature Reused Network for Defect Detection in Steel Images

Abstract Accurate detection of surface defects for steel is essential to improve surface quality and service life. Deep learning (DL) used in steel surface defect detection can solve the problems of low efficiency and poor accuracy of traditional manual detection. The classic YOLOv5 as a DL method is used to accomplish defect detection tasks without attention mechanisms, resulting in a loss of global information. Besides, it is difficult to complete complex network detection tasks with low-configuration hardware, especially for surface defects with complex defect types and variable defect sizes. To solve these issues, this paper introduces an improved global feature reuse and hardware-aware YOLOv5 by using BoTNet, RepGhost, and EfficientRep model (BGE-YOLOv5). The multi-head self-attention layer is used to obtain global information and only part of the convolutional layers is replaced to avoid the excessive computational cost. The RepGhost model is introduced to extract the remaining feature information for feature reuse. EfficientRep is used to replace the original structure to achieve hardware-aware and to balance the detection veracity and efficiency. The distance IOU is replaced by SCYLLA-IOU to accelerate the iteration and improve stability. The results of the framework on the surface defect database (NEU-DET) show that BGE-YOLOv5 achieves a mean average precision of 79.5%, which is 10.3% greater than the baseline. The proposed BGE-YOLOv5 has a better performance in steel surface defect detection.

Defect recognition and classification from ultrasonic phased array total focusing method imaging based on domain adaption

Abstract With the rapid development of China’s economy, the demand for oil and gas resources is continuously increasing, leading to the expansion of the scale of oil and gas long-distance pipelines. Consequently, the risk of safety incidents in oil and gas pipelines is on the rise. Welding defects are identified as one of the significant factors contributing to safety incidents in long-distance oil and gas pipelines. In comparison to traditional conventional ultrasonic detection techniques, which exhibit low efficiency and accuracy, ultrasonic phased array detection technology has the capability to accurately detect welding defects in oil and gas long-distance pipelines. This is particularly true with the development of total focusing method imaging technology, enabling quantitative defect analysis and localization in oil and gas long-distance pipelines.However, in the current scenario, analyzing the types of defects in massive data from long-distance pipelines requires human intervention, leading to low detection efficiency and subjective influence on result analysis. Addressing challenges such as low efficiency in human evaluation of defects in ultrasonic phased array detection, low accuracy in traditional image recognition methods, strong subjectivity in manually extracting features, and the absence of a standardized industrial dataset for ultrasonic phased array detection defects, especially in the case of limited data on ultrasonic phased array detection of welding defects, this study focuses on small sample defects in phased array detection images. The study proposes an improved ResNet50 algorithm through domain adaptation in a deep neural network, trained on a large-scale model data, and then applied to ultrasonic phased array detection data. This approach achieves accurate identification and classification of typical volumetric defects (such as pores) and typical area defects (such as cracks) in the phased array detection of welding defects, laying the foundation for intelligent recognition of defects in long-distance oil and gas pipelines.

Radiopharmaceuticals Targeting Gastrin-Releasing Peptide Receptor for Diagnosis and Therapy of Prostate Cancer.

The high incidence and heavy disease burden of prostate cancer (PC) require accurate and comprehensive assessment for appropriate disease management. Prostate-specific membrane antigen (PSMA) positron emission tomography (PET) cannot detect PSMA-negative lesions, despite its key role in PC disease management. The overexpression of gastrin-releasing peptide receptor (GRPR) in PC lesions reportedly performs as a complementary target for the diagnosis and therapy of PC. Radiopharmaceuticals derived from the natural ligands of GRPR have been developed. These radiopharmaceuticals enable the visualization and quantification of GRPR within the body, which can be used for disease assessment and therapeutic guidance. Recently developed radiopharmaceuticals exhibit improved pharmacokinetic parameters without deterioration in affinity. Several heterodimers targeting GRPR have been constructed as alternatives because of their potential to detect tumor lesions with a low diagnostic efficiency of single target detection. Moreover, some GRPR-targeted radiopharmaceuticals have entered clinical trials for the initial staging or biochemical recurrence detection of PC to guide disease stratification and therapy, indicating considerable potential in PC disease management. Herein, we comprehensively summarize the progress of radiopharmaceuticals targeting GRPR. In particular, we discuss the impact of ligands, chelators, and linkers on the distribution of radiopharmaceuticals. Furthermore, we summarize a potential design scheme to facilitate the advancement of radiopharmaceuticals and, thus, prompt clinical translation.

Improvement of defect detection for steel based on YOLOv7 algorithm

Abstract Working on the conundrum of low efficiency of strip surface defect detection, a surface defect detection algorithm for boards and strips leveraging YOLOV7 is introduced. First, the global attention mechanism is set up to enhance global information interaction and expression capabilities, and improve detection performance; in second place, the C2f component is integrated into the feature pyramid network to the lightweight pattern; ultimately, Focal-EIoU is exerted to replace loss function of the YOLOv7 pattern to solve the divination box and the problem of wrong amplification of length and width, improving the accuracy of defect classification and positioning. The outcomes reveal that the precision of the elevated algorithm has been boosted by about 4.3%, the recall rate has reached 78.3%, and mAP increased by 3.4%. This technique exhibits high accuracy and efficiency in detecting surface defects on the tape.

Compressive strength detection of tunnel lining using hyperspectral images and machine learning

Traditional tunnel lining strength detection techniques are mostly contact-based, with relatively low detection efficiency. This study innovatively proposes hyperspectral imaging method to rapidly detect tunnel concrete lining strength from a machine vision perspective. Hyperspectral cameras were employed in indoor experiments to capture hyperspectral images of concrete specimens with different compressive strength levels. The differences of concrete strength based on hyperspectral reflectance characteristics were analysed using hyperspectral images and machine learning algorithms. Firstly, the K-Nearest Neighbors (KNN) classification algorithm was used to predict the classification of the concrete hyperspectral dataset with accuracy mostly exceeding 90 %. The results indicate distinctive differences in hyperspectral reflectance characteristics among concrete specimens. Furthermore, compressive strength prediction of different concrete specimens was carried out using Principal component regression (PCR), Partial least squares regression (PLSR), and Least Squares Support Vector Machine (LSSVM) machine learning models on both original and Savitzky-Golay(S-G) processed spectral data. LSSVM and PLSR models performed excellently in the visible light spectrums(400–1000 nm), with LSSVM excelling in the near-infrared spectrums(900–1700 nm). Finally, the feasibility of using Hyperspectral imaging(HSI) technology to detect tunnel lining strength was demonstrated at the shield tunnel model site. The predicted results were validated by combining with strength values measured by the rebound hammer, and presented promoising research path for automated non-destructive detection of concrete tunnel lining strength.

Suppressing Thermal Noise to Sub-Millikelvin Level in a Single-Spin Quantum System Using Realtime Frequency Tracking.

A single nitrogen-vacancy (NV) center in a diamond can be used as a nanoscale sensor for magnetic field, electric field or nuclear spins. Due to its low photon detection efficiency, such sensing processes often take a long time, suffering from an electron spin resonance (ESR) frequency fluctuation induced by the time-varying thermal perturbations noise. Thus, suppressing the thermal noise is the fundamental way to enhance single-sensor performance, which is typically achieved by utilizing a thermal control protocol with a complicated and highly costly apparatus if a millikelvin-level stabilization is required. Here, we analyze the real-time thermal drift and utilize an active way to alternately track the single-spin ESR frequency drift in the experiment. Using this method, we achieve a temperature stabilization effect equivalent to sub-millikelvin (0.8 mK) level with no extra environmental thermal control, and the spin-state readout contrast is significantly improved in long-lasting experiments. This method holds broad applicability for NV-based single-spin experiments and harbors the potential for prospective expansion into diverse nanoscale quantum sensing domains.

Lightweight-detection: The strip steel surface defect identification based on improved YOLOv5d

The surface defect of strip steel seriously affects the product quality, the current identification methods have the inherent defects of low detection efficiency and poor detection accuracy, and it is important an improved an algorithm based on the modified YOLOv5 framework to enhance the defect detection effectiveness and accuracy. To create a more lightweight network model, the C3 module and part of the convolutional structure in the original YOLOv5 network were replaced with the GhostBottleneck structure. Additionally, the Coordinate Attention (CA) mechanism was incorporated into the Backbone section to compensate for the original model's lack of attention mechanisms. To address the angular mismatch between the actual frame and the predicted bounding box, a limitation overlooked by the CIOU loss function was further refined. Experimental results demonstrate that the enhanced YOLOv5s-CSG model outperformed the original YOLOv5s by 7.3 %, achieving a significant 37.9 % reduction in model size and an mAP value of 77.5 %. Furthermore, the detection precision and speed were notably higher compared to several widely-used algorithms. The proposed YOLOv5s-CSG model is well-suited for deployment on mobile devices and shows great potential for rapidly and accurately detecting both the type and location of strip surface defects, surpassing the performance of existing methods.

Object Detection Algorithms Based on Deep Learning: A Review

With the continuous development of deep learning, object detection algorithms based on deep learning have made significant progress in the field of computer vision, widely applied in areas such as autonomous driving, industrial inspection, agriculture, transportation, and medicine. Traditional object detection algorithms face issues such as low detection efficiency and poor robustness. However, deep learning-based object detection algorithms significantly enhance detection accuracy and generalization by learning low-level and high-level image features. This article first introduces traditional object detection algorithms and their existing problems, then elaborates on the main processes, innovations, advantages, disadvantages, and experimental results on datasets of deep learning-based object detection algorithms. It focuses on the development of Two-Stage and One-Stage object detection algorithms, and provides an outlook on the future development of object detection algorithms, discussing challenges such as the coordination of detection speed and accuracy, difficulties in detecting small objects, real-time detection tasks, and multi-modal fusion applications, and proposes possible future directions.

BS-YOLOV8: an intelligent detection model for bearing pin support-piece states of high-rise building machine

Abstract As the main support part of the working platform of a high-rise building machine, the bearing pin support (BPS) plays a crucial role in the safety and stability of the platform, the conventional method has the problems of low detection efficiency, low accuracy, and high cost. To improve the accuracy and robustness of the detection algorithm under weak light, this paper proposes an intelligent detection algorithm for the BPS-piece states of the BS-YOLOV8, to improve the feature map utilization and reduce the model leakage detection error detection rate, Swin transformer is used to improve the YOLOV8 backbone network. In addition, the BiFormer attention mechanism is used to weigh the feature map to solve the problem of feature information loss in different feature layers and weak lighting conditions, and then the Scylla-IOU loss function is used instead of the original localization loss function to guide the model to learn to generate a predicted bounding box closer to the real target bounding box. Finally, the BS-YOLOV8 algorithm is used to compare with its classical algorithm on the self-constructed dataset of this study, The results show that the mAP0.5, mAP0.5:0.95, and FPS values of the BS-YOLOV8 algorithm reach 97.9%, 96.3% and 40 under normal lighting. The mAP0.5 value reaches 87.6% under low light conditions, which effectively solves the problems of low detection efficiency and poor detection under low light conditions, and is superior compared to other algorithms.

Detection of Multi-Layered Bond Delamination Defects Based on Full Waveform Inversion.

This study aimed to address the challenges encountered in traditional bulk wave delamination detection methods characterized by low detection efficiency. Additionally, the limitations of guided wave delamination detection methods were addressed, particularly those utilizing reflected waves, which are susceptible to edge reflections, thus complicating effective defect extraction. Leveraging the full waveform inversion algorithm, an innovative approach was established for detecting delamination defects in multi-layered structures using ultrasonic guided wave arrays. First, finite element modeling was employed to simulate guided wave data acquisition by a circular array within an aluminum-epoxy bilayer structure with embedded delamination defects. Subsequently, the full waveform inversion algorithm was applied to reconstruct both regular and irregular delamination defects. Analysis results indicated the efficacy of the proposed approach in accurately identifying delamination defects of varying shapes. Furthermore, an experimental platform for guided wave delamination defect detection was established, and experiments were conducted on a steel-cement bilayer structure containing an irregular delamination defect. The experimental results validated the exceptional imaging precision of our proposed technique for identifying delamination defects in multi-layered boards. In summary, the proposed method can accurately determine both the positions and sizes of defects with higher detection efficiency than traditional pulse-echo delamination detection methods.