Missed Detection Rate Research Articles

Minimal Defect Detection on End Face of Lithium Battery Shells

Lithium batteries represent a pivotal technology in the advancement of renewable energy, and their enhanced performance and safety are vital to the attainment of sustainable development goals. To solve the issue of the high missed detection rate of minimal defects on end face of lithium battery shells, a novel YOLO-based Minimal Defect Detection algorithm, named YOLO-MDD, is proposed. Firstly, aimed at the problem of insufficient data, a dataset of defects on the end face of lithium battery shells is constructed and annotated. Secondly, a YOLO-MDD network which includes a feature extraction module and a four-scale detection head for detecting defects of various scales is improved. Here, deformable convolution and an attention module are ingeniously embedded into the backbone of YOLO to capture more detailed and accurate information on object defects, and the four-scale head is used to handle the significant differences in the size and shape of defects on lithium battery shells. Finally, a hybrid loss including localization loss with normalized Wasserstein distance (NWD), classification loss, and confidence loss is designed to optimize our model to further enhance its sensitivity to minimal defects. The experimental results show that the proposed YOLO-MDD has a mean average precision of 80% for the defect detection of the lithium battery shells, especially with a minimal defect rust spots mean average precision of 74.1% and a recall rate of 71.5%, which is superior compared with other mainstream detection algorithms and provides the technical support necessary to achieve the goals of energy and environmental sustainability.

Research on Deep Learning Model Enhancements for PCB Surface Defect Detection

With the miniaturization and increasing complexity of electronic devices, the accuracy and efficiency of printed circuit board (PCB) defect detection are crucial to ensuring product quality. To address the issues of small defect sizes and high missed detection rates in PCB surface inspection, this paper proposes an enhanced YOLOv8s model which not only improves detection performance but also achieves a lightweight design. Firstly, the Nexus Attention module is introduced, which organically integrates multiple attention mechanisms to further enhance feature extraction and fusion capabilities, improving the model’s learning and generalization performance. Secondly, an improved CGFPN network is designed to optimize multi-scale feature fusion, significantly boosting the detection of small objects. Additionally, the WaveletUnPool module is incorporated, leveraging wavelet transform technology to refine the upsampling process, accurately restoring detailed information and improving small-object detection in complex backgrounds. Lastly, the C2f-GDConv module replaces the traditional C2f module, reducing the number of model parameters and computational complexity while maintaining feature extraction efficiency. Comparative experiments on a public PCB dataset demonstrate that the enhanced model achieved a mean average precision (mAP) of 97.3% in PCB defect detection tasks, representing a 3.0% improvement over the original model, while reducing Giga Floating Point Operations (GFLOPs) by 26.8%. These enhancements make the model more practical and adaptable for industrial applications, providing a solid foundation for future research.

RS-DETR: An Improved Remote Sensing Object Detection Model Based on RT-DETR

Object detection is a fundamental task in computer vision. Recently, deep-learning-based object detection has made significant progress. However, due to large variations in target scale, the predominance of small targets, and complex backgrounds in remote sensing imagery, remote sensing object detection still faces challenges, including low detection accuracy, poor real-time performance, high missed detection rates, and high false detection rates in practical applications. To enhance remote sensing target detection performance, this study proposes a new model, the remote sensing detection transformer (RS-DETR). First, we incorporate cascaded group attention (CGA) into the attention-driven feature interaction module. By capturing features at different levels, it enhances the interaction between features through cascading and improves computational efficiency. Additionally, we propose an enhanced bidirectional feature pyramid network (EBiFPN) to facilitate multi-scale feature fusion. By integrating features across multiple scales, it improves object detection accuracy and robustness. Finally, we propose a novel bounding box regression loss function, Focaler-GIoU, which makes the model focus more on difficult samples, improving detection performance for small and overlapping targets. Experimental results on the satellite imagery multi-vehicles dataset (SIMD) and the high-resolution remote sensing object detection (TGRS-HRRSD) dataset show that the improved algorithm achieved mean average precision (mAP) of 78.2% and 91.6% at an intersection over union threshold of 0.5, respectively, which is an improvement of 2.0% and 1.5% over the baseline model. This result demonstrates the effectiveness and robustness of our proposed method for remote sensing image object detection.

WITHDRAWN: Design and experiment of a deviation information detection mechanism for sugar beet harvesters based on agricultural machinery and agronomy integration

To address large geometric shape differences, poor ridge distribution straightness and the uncertain spatial distribution of beet roots during the harvesting period, as well as damage caused by the inaccurate row correction detection mechanisms of combine harvesters, this study conducts a design analysis and performance tests of deviation information detection mechanisms based on the integration of agricultural machinery and agronomy. The agronomic process of mechanized sugar beet production was analyzed, the geometric dimensions of root tubers under typical planting patterns of typical varieties in main sugar beet production areas were measured, and a three-dimensional geometric model of beet root tubers was established. A device for measuring ridge shape and root tuber distribution was designed, and agronomic parameters during the harvesting period, such as plant spacing, ridge height, unearthed height, and deviation distance of beet root tubers, were measured and analyzed. A spatial model of ridge shape and beet root tuber growth distribution on the ridge during harvesting was established. The overall structure of the deviation information detection mechanism was analyzed and designed, and the structural forms and key parameters of key mechanisms, such as left‒right swing detection and up‒down floating profiling, were designed on the basis of agronomic parameter analysis. Using the missed detection rate as the evaluation index, field performance tests of the deviation information detection mechanism were conducted. The results revealed that when the average forward speed of the harvester was 0.84 m/s, the average missed detection rate of the deviation information detection mechanism was 0.56%. This method has high adaptability and detection accuracy and achieves a high level of integration for agricultural machinery (detection spatial region of the detection mechanism) and agronomy (beet root distribution spatial region). This study provides a technical basis and methodological reference for improving the quality and efficiency of automatic row correction combined with harvesting operations for crops such as sugar beets.

Missed detection probability evaluation for the RAIM based on worst-case fault magnitude searching

Missed detection probability is a critical metric for the integrity performance of receiver autonomous integrity monitoring (RAIM) in the presence of faults. The traditional missed detection probability evaluation method for RAIM is limited by impractical time consumption because of the absence of accurate searching interval for the magnitude of a worst-case fault. To address this issue, the searching interval for the magnitude of a worst-case fault is constructed by the combination of minimum detectable magnitude and minimum hazardous magnitude, and the searching interval adjustment is designed to avoid the absence of worst-case fault magnitude so that the maximum missed detection probability can be accurately evaluated. The simulation result indicates that the proposed method can achieve higher accuracy for the worst-case fault magnitude searching. Furthermore, the accuracy of worldwide evaluated missed detection rate can achieve an improvement of 57·66% at most by the proposed method for the different classical RAIM algorithms.

Cleaning method for abnormal energy big data based on sparse self-coding

In order to reduce the interference of abnormal energy big data and improve the accuracy of anomaly cleaning and detection, this paper proposes a method for cleaning abnormal energy big database based on sparse self-coding. Firstly, the abnormal data detection method based on multi-criteria evaluation is used to analyze the spectral feature distribution of abnormal energy big data. By using chaotic time series reconstruction model, robust local weighted regression analysis and sparse self-coding method, the feature decomposition of time series of energy data is realized. Then, according to the periodicity of the original sequence, the original sequence is segmitated adaptively to represent the morphological characteristics of the abnormal energy data sub-sequences driven by dynamic carbon emissions, and the anomaly index of each sub-sequence is obtained by AFCM algorithm. Secondly, an energy anomaly evaluation model based on LOF value is established. Finally, the output decision function of data cleaning is constructed to realize the abnormal energy big data cleaning. The test results show that the error detection rate of this method is 0.24%, the missing detection rate is 0.27%, the cleaning rate can reach 99.49%, and the cleaning time is less than 2s.

Aircraft skin defect detection algorithm based on improved YOLOv8

To solve the problem that the traditional aircraft skin defect detection relies on human eye observation, which leads to reduced efficiency due to human eye fatigue and limited individual cognition, an aircraft skin defect detection algorithm based on improved YOLOv8 is proposed. The data enhancement method is improved, and a slice reasoning + mosaic data enhancement method is proposed; the residual block is integrated into the feature extraction network to enhance the network expression ability and improve the accuracy of the model in the aircraft skin defect detection task; the three-branch (Triplet) attention module is applied to improve the feature fusion network to reduce the false detection rate and missed detection rate of small target samples; the structure of the detection head is optimized so that the network can better combine shallow information with deep information. Experimental results show that compared with the latest YOLOv8 algorithm, the improved algorithm improves the mean average precision (mAP) and recall rate (Recall) on the aircraft skin defect dataset by 3.6%and respectively 3.7%; and improves the mean average precision and recall rate on the public dataset VOC2007 by 2.9%and 2.2%.

Enhanced Abandoned Object Detection through Adaptive Dual-Background Modeling and SAO-YOLO Integration

Abandoned object detection is a critical task in the field of public safety. However, existing methods perform poorly when detecting small and occluded objects, leading to high false detection and missed detection rates. To address this issue, this paper proposes an abandoned object detection method that integrates an adaptive dual-background model with SAO-YOLO (Small Abandoned Object YOLO). The goal is to reduce false and missed detection rates for small and occluded objects, thereby improving overall detection accuracy. First, the paper introduces an adaptive dual-background model that adjusts according to scene changes, reducing noise interference in the background model. When combined with an improved PFSM (Pixel-based Finite State Machine) model, this enhances detection accuracy and robustness. Next, a network model called SAO-YOLO is designed. Key improvements within this model include the SAO-FPN (Small Abandoned Object FPN) feature extraction network, which fully extracts features of small objects, and a lightweight decoupled head, SODHead (Small Object Detection Head), which precisely extracts local features and enhances detection accuracy through multi-scale feature fusion. Finally, experimental results show that SAO-YOLO increases mAP@0.5 and mAP@0.5:0.95 by 9.0% and 5.1%, respectively, over the baseline model. It outperforms other advanced detection models. Ultimately, after a series of experiments on the ABODA, PETS2006, and AVSS2007 datasets, the proposed method achieved an average detection precious of 91.1%, surpassing other advanced methods. It significantly outperforms other advanced detection methods. This approach notably reduces false and missed detections, especially for small and occluded objects.

A robust detection scheme against selective forwarding attacks in wireless sensor networks under variable harsh environments

Event-driven wireless sensor networks (EWSNs) often operate in harsh environments and are susceptible to selective forwarding attacks, where malicious nodes selectively drop packets. These attacks are difficult to detect due to similarities with normal node behavior under adverse conditions. To address this, we propose a detection scheme combining Variable Autoencoder (VAE) and Gaussian Mixture Model (GMM) with an Autoencoder (AE). The scheme extracts features from the node's single round forwarding rate (SFR), with VAE generating latent variables and GMM clustering them to differentiate behaviors. The AE model, trained on data representing normal behavior, uses reconstruction error to identify malicious nodes. Simulation results show that our scheme improves network throughput and achieves low False Detection Rate (FDR) and Missed Detection Rate (MDR), with both rates as low as 0.5% in normal conditions and below 2% in mobile harsh environments. With a computational complexity of O(n), this method is suitable for resource-constrained environments, demonstrating superior robustness compared to existing approaches.

A Dynamic Sandbox Detection Technique in a Private Cloud Environment

In specific private cloud scenarios, how to defend against malicious software and ensure data security is one of the current research hotspots, and sandbox is an important detection method. This paper proposes a dynamic behavior detection technique based on sandboxing, which real-time monitors and analyzes malicious software behavior. By improving the sandbox behavior weight, integrating virtual resources, and designing fine-grained access control, the detection accuracy and efficiency are enhanced based on zero trust access control system. The simulated attacks are identified on the testing platform, drawing knowledge graphs, achieving effective discovery and tracing. Meanwhile, this paper verified through experiments that the system consumption of the detection method is within an acceptable range, expanding the detection range and reducing the missed detection rate.

RCYOLO: an innovative surface defect detection model for magnetic blocks integrating RevCol and Yolov8s

ABSTRACT The surface defect detection of magnetic blocks faces challenges such as low accuracy, high missed detection rates, and weak anti-interference capabilities. Additionally, existing models are often large in size and require significant computational resources. To address these issues, this article integrated the Yolov8 framework and designed the RCYOLO model to improve detection performance and resource utilization. Firstly, we used RevCol as the backbone, and explored its optimal number of sub-networks to reduce model parameters and complexity, as well as improve model robustness. Secondly, we removed some deep structures from Yolov8s to simplify the model, making it easier to deploy. Afterwards, we developed a more efficient feature extraction module, C2f_SA, and replaced all existing modules with it to compensate for accuracy loss caused by simplification and improve overall detection performance. Finally, we optimized reg_max to explore the best minimum value for detecting defects in magnetic blocks to reduce unnecessary computational burden. Experimental results show that compared to Yolov8s, although RCYOLO’s mAP0.5 was reduced by 0.2%, it significantly reduced the model parameters, FLOPs, and model size by 51.64%, 30.31%, and 50.22%, respectively, achieving a better balance between accuracy and resources. Moreover, RCYOLO demonstrated good detection performance compared to other improved models.

Gastrointestinal image stitching based on improved unsupervised algorithm.

Image stitching is a traditional but challenging computer vision task. The goal is to stitch together multiple images with overlapping areas into a single, natural-looking, high-resolution image without ghosts or seams. This article aims to increase the field of view of gastroenteroscopy and reduce the missed detection rate. To this end, an improved depth framework based on unsupervised panoramic image stitching of the gastrointestinal tract is proposed. In addition, preprocessing for aberration correction of monocular endoscope images is introduced, and a C2f module is added to the image reconstruction network to improve the network's ability to extract features. A comprehensive real image data set, GASE-Dataset, is proposed to establish an evaluation benchmark and training learning framework for unsupervised deep gastrointestinal image splicing. Experimental results show that the MSE, RMSE, PSNR, SSIM and RMSE_SW indicators are improved, while the splicing time remains within an acceptable range. Compared with traditional image stitching methods, the performance of this method is enhanced. In addition, improvements are proposed to address the problems of lack of annotated data, insufficient generalization ability and insufficient comprehensive performance in image stitching schemes based on supervised learning. These improvements provide valuable aids in gastrointestinal examination.

Lightweight Substation Equipment Defect Detection Algorithm for Small Targets.

Substation equipment defect detection has always played an important role in equipment operation and maintenance. However, the task scenarios of substation equipment defect detection are complex and different. Recent studies have revealed issues such as a significant missed detection rate for small-sized targets and diminished detection precision. At the same time, the current mainstream detection algorithms are highly complex, which is not conducive to deployment on resource-constrained devices. In view of the above problems, a small target and lightweight substation main scene equipment defect detection algorithm is proposed: Efficient Attentional Lightweight-YOLO (EAL-YOLO), which detection accuracy exceeds the current mainstream model, and the number of parameters and floating point operations (FLOPs) are also advantageous. Firstly, the EfficientFormerV2 is used to optimize the model backbone, and the Large Separable Kernel Attention (LSKA) mechanism has been incorporated into the Spatial Pyramid Pooling Fast (SPPF) to enhance the model's feature extraction capabilities; secondly, a small target neck network Attentional scale Sequence Fusion P2-Neck (ASF2-Neck) is proposed to enhance the model's ability to detect small target defects; finally, in order to facilitate deployment on resource-constrained devices, a lightweight shared convolution detection head module Lightweight Shared Convolutional Head (LSCHead) is proposed. Experiments show that compared with YOLOv8n, EAL-YOLO has improved its accuracy by 2.93 percentage points, and the mAP50 of 12 types of typical equipment defects has reached 92.26%. Concurrently, the quantity of FLOPs and parameters has diminished by 46.5% and 61.17% respectively, in comparison with YOLOv8s, meeting the needs of substation defect detection.

Detecting selective forwarding attacks on cloud

—The Internet of Things (IoT) technology is now ubiquitous in both daily life and industrial production. Wireless Sensor Networks (WSNs) are a crucial part of the IoT infrastructure, providing the underlying network. However, WSNs are vulnerable to attacks due to challenging environmental conditions and their open communication nature. One particularly elusive threat is the selective forwarding attack, where malicious nodes intentionally drop parts or all of the received data packets, causing information loss and delays. This paper addresses the transition from the limited resources of central processing units (CPUs) to the expansive capabilities of cloud platforms within the IoT framework. By harnessing the robust computing power of cloud platforms, we investigate the effectiveness of the Time Convolution Network (TCN) time series prediction algorithm in detecting malicious nodes. Comparative results demonstrate that our proposed scheme achieves a Miss Detection Rate (MDR) of below 1% and a False Detection Rate (FDR) of less than 5%.

Prevalence and distribution of serological characteristics of weak ABO subgroups in the Chinese population

ObjectivesThis study aimed to determine the true frequency of weak ABO subgroups and to investigate the serological characteristics of various subgroup alleles in the Chinese population. MethodsA total of 2,945,643 blood samples were collected from January 2009 to December 2017. After initial screening and re-examination using automatic blood group analyzers, all ABO-discrepant samples were confirmed by standard serological analysis and molecular detection by DNA sequencing. The true frequency of weak ABO subgroups was determined by the number of ABO subgroup donors and the missed detection rate. The ABO antigen expressions corresponding to subgroup alleles were analyzed by the agglutination intensity of red blood cells. ResultsThe detection rate of ABO subgroups was 0.031 % (927/2,945,643). Considering the missed detection rate (27.81 %), the true frequency of ABO subgroups in the Chinese population was 0.044 %. The three most common genetic variations among blood donors in Shanghai were BA.04, BW.12 and BA.02. BW.03 showed the weakest B antigen expression (6.00 ± 1.97) and Bvar-1 the strongest (9.20 ± 1.10). ConclusionMany ABO subgroups were missed. BA.04 was found to be the most common subgroup allele in the Chinese population. Different ABO subgroup alleles exhibit different ABO antigen expression patterns.

Online Monitoring Technology for Cable Insulation in Distribution Networks Based on Line Conduction Characteristics

Due to the increasing load of the power grid and the rapid expansion of cable line construction, monitoring cable insulation performance has become very important. To address this challenge, this paper proposes an online monitoring system for cable insulation in distribution networks based on line conduction characteristics. The system combines advanced signal acquisition technology, data processing methods, and deep learning (DL) algorithms to enable real-time monitoring and accurate evaluation of cable insulation status. The results demonstrate that the system excels in real-time monitoring of cable insulation status. Compared to traditional methods, the system proposed in this paper shows significant advantages in key indicators such as missed detection rate, fault location accuracy, recognition speed, and recall rate. Specifically, the system effectively reduces the missed detection rate, improves the accuracy of fault location, accelerates identification speed, and enhances the recall rate, enabling more comprehensive detection of insulation faults.

Collaborative Obstacle Detection for Dual USVs Using MGNN-DANet with Movable Virtual Nodes and Double Attention

To reduce missed detections in LiDAR-based obstacle detection, this paper proposes a dual unmanned surface vessels (USVs) obstacle detection method using the MGNN-DANet template matching framework. Firstly, point cloud templates for each USV are created, and a clustering algorithm extracts suspected targets from the point clouds captured by a single USV. Secondly, a graph neural network model based on the movable virtual nodes is designed, introducing a neighborhood distribution uniformity metric. This model enhances the local point cloud distribution features of the templates and suspected targets through a local sampling strategy. Furthermore, a feature matching model based on double attention is developed, employing self-attention to aggregate the features of the templates and cross-attention to evaluate the similarity between suspected targets and aggregated templates, thereby identifying and locating another USV within the targets detected by each USV. Finally, the deviation between the measured and true positions of one USV is used to correct the point clouds obtained by the other USV, and obstacle positions are annotated through dual-view point cloud clustering. Experimental results show that, compared to single USV detection methods, the proposed method reduces the missed detection rate of maritime obstacles by 7.88% to 14.69%.

Automated inspection approach for OCA particles in multi-layered cover glass of display module assembly

Abstract CG (Cover Glass) is a critical component of electronic screens, and its manufacturing quality is closely relevant to the display effect. To satisfy the requirement of quality control, a machine vision system for real-time inspection of particles in the OCA (Optically Clear Adhesive) layer of CG is presented in this paper. With a brief description of the optical logic of particle imaging and the design of the vision system, our emphasis is put on the post-processing image analysis. To align particles regions in the CG under multi-mode imaging, a spatial alignment calibration algorithm is proposed with perspective distortion correction to calculate the triaxial offset. Then, a CLAHE+PM filtering is adopted to enhance the contrast of the particle. Furthermore, a Meanshift method combined with adaptive local thresholding is proposed to extract the contours of tiny particles. Finally, to distinguish between multiple layers of particles in the CG and detect OCA particles, a combination of fast background reconstruction and Averaged Stochastic Gradient Descent-Support Vector Machine is used. According to in-line experiments and tests, our system can find out a majority of the OCA particles with a PR (over-detection rate) of 1.31% and a PM (miss detection rate) of 0.33% for over 10 000 CG samples.

Road Design and Traffic Detection Methods for Autonomous Driving Scenarios

With the rapid promotion of autonomous driving technology, it is extremely important to scientifically anticipate the related technologies and analyze their possible impact on urban road systems. The accuracy of detection and localization of traffic elements of autonomous driving is closely related to the ability of autonomous driving devices to make control decisions and the safety of autonomous driving. The study designs a new high-speed road driving scheme based on autonomous driving by analyzing the challenges related to urban traffic that may be brought about by unmanned driving. On the basis of the faster R-CNN algorithm, the context information around the target is introduced to locate and detect small-scale traffic signs. A new pedestrian detection model is designed, which is based on the feature pyramid network and introduces the SE module to highlight the features of the visible part of the pedestrian and reduce the missed detection rate caused by inter-class occlusion. The improved traffic sign detection framework improves the detection accuracy by 18.91% compared to the original faster R-CNN, while the enhanced pedestrian inspection method improves the detection accuracy by 14.00%. For both traffic sign detection and pedestrian detection accuracy and speed are improved compared to the original method.

Small-target smoking detection algorithm based on improved YOLOv5

The use of general target detection algorithms for small-target detection is computationally costly and has a high missed detection rate. A lightweight small-target detection model based on YOLOv5 is proposed to address this issue.First, a maximum pooling layer is introduced to reduce the number of calculations. Second, Shuffle_Conv is designed to replace the ordinary convolutional layer to reduce model parameters. To further compress the model, the Add fusion method is used in the C3 module, while the GAC3 layer is designed with GhostNet. Finally, Mosaic_9 is introduced to improve the small-target detection without increasing the number of calculations. Compared with YOLOv5, computation and parameters of the improved model are reduced by 84.9% and 39.1%, respectively, and the accuracy is improved by 2%, which is more obvious than that of the original model.