Missed Detection Rate Research Articles

An improved detection method of GNSS faults with fractional information divergence

Early detection of faults guarantees the integrity of navigation systems. The standard receiver autonomous integrity monitoring (RAIM) incorporated with the Kullback-Leibler divergence (KLD) has been proposed to improve detection performance. However, the KLD-RAIM cannot ensure a high detection rate in the presence of small or growing faults. This paper broadens the existing KLD-RAIM to further enhance the detection ability for satellite navigation-based aviation applications. The Cumulative residual KL information (CRKL) replaces the KLD to quantify the dissimilarity between the current and normal cumulative distribution functions for the CRKL-RAIM construction. Then CRKL is extended to fractional orders using Riemann–Louville (RL) calculus to form fractional CRKL-RAIM for detection sensitivity enhancement. Simulations using static GNSS station data were conducted to compare four methods: the standard RAIM, existing KLD-RAIM, the proposed CRKL-RAIM, and fractional CRKL-RAIM. When dealing with small step faults, the detection sensitivity increases progressively, with the fractional CRKL-RAIM capable of detecting 10-meter faults. Regarding growing faults, the standard RAIM fails to detect and KLD-RAIM barely detects faults around 600 s with 50% missed detection rate. By contrast, CRKL-RAIM and fractional CRKL-RAIM detect faults around 100 and 200 s in advance, respectively, without any missed detection events.

Mining security assessment in an underground environment using a novel face recognition method with improved multiscale neural network

Overstaffing production in underground coal mining is not convenient for daily management, and incomplete information of coal miners hinders the rescue process of firefighters during mine accidents. To address this safety sustainability issue, a novel face recognition method based on an improved multiscale neural network is proposed in this paper. A new depthwise separable (DS)-inception block is designed and a joint supervised loss function based on center loss theory is developed to construct a new multiscale model. The miniers can be recognized in the harsh underground environment during the life rescue. Experimental results show that the accuracy, recall and F1-score indexes of the proposed method for the miner face recognition in the underground mining environment are 97.26%, 94.17% and 95.42%, respectively. Transfer model with joint supervised loss can effectively improve the recognition accuracy by about 0.5–1.5%. In addition, the average recognition accuracy of the proposed face recognition method achieves to 91.34% and the miss detection rate is less than 5% in the dugout tunnel of coal mine.

Multi-scale coal and gangue detection in dense state based on improved Mask RCNN

Coal and gangue intelligent sorting accounts for a vital proportion of modern coal mines, which takes on critical significance in boosting clean coal utilization. Aiming at the problem of low recognition accuracy that arises from the large diversity in particle size of raw coal and the adhesion and half-occlusion between them, A Mask RCNN-based instance segmentation network for coal and gangue image is proposed. First, multi-branch parallel feature extraction bottlenecks are developed to build a lightweight backbone with a mixed attention mechanism and self-correcting convolution incorporated to enhance the feature representation of targets. Subsequently, a necknet is developed, aggregating the context feature information of the backbone from channel and space to enhance the position and boundary information of targets. Lastly, the effectiveness of this method is fully validated by ablation and test experiments using self-built coal and gangue RGB image datasets include varieties of characteristics as input. As indicated by the experimental results, the above-described method is capable of enhancing the segmentation ability of stacked and adhered coal and gangue and reducing the missed detection rate of small targets. ISNet_CG achieves mAP, mIoU, F1-scores, and FPS of 98.0, 96.5, 0.987, and 24.2 FPS, respectively, such that the correct category and location can be provided for coal and gangue in a dense state.

UAV-YOLOv8: A Small-Object-Detection Model Based on Improved YOLOv8 for UAV Aerial Photography Scenarios.

Unmanned aerial vehicle (UAV) object detection plays a crucial role in civil, commercial, and military domains. However, the high proportion of small objects in UAV images and the limited platform resources lead to the low accuracy of most of the existing detection models embedded in UAVs, and it is difficult to strike a good balance between detection performance and resource consumption. To alleviate the above problems, we optimize YOLOv8 and propose an object detection model based on UAV aerial photography scenarios, called UAV-YOLOv8. Firstly, Wise-IoU (WIoU) v3 is used as a bounding box regression loss, and a wise gradient allocation strategy makes the model focus more on common-quality samples, thus improving the localization ability of the model. Secondly, an attention mechanism called BiFormer is introduced to optimize the backbone network, which improves the model's attention to critical information. Finally, we design a feature processing module named Focal FasterNet block (FFNB) and propose two new detection scales based on this module, which makes the shallow features and deep features fully integrated. The proposed multiscale feature fusion network substantially increased the detection performance of the model and reduces the missed detection rate of small objects. The experimental results show that our model has fewer parameters compared to the baseline model and has a mean detection accuracy higher than the baseline model by 7.7%. Compared with other mainstream models, the overall performance of our model is much better. The proposed method effectively improves the ability to detect small objects. There is room to optimize the detection effectiveness of our model for small and feature-less objects (such as bicycle-type vehicles), as we will address in subsequent research.

Feature extraction-reduction and machine learning for fault diagnosis in PV panels

With the rapid expansion and installation of Photovoltaic (PV) power plants, developing a proper Fault Detection and Diagnosis (FDD) strategy has become a significant issue within the objective to organize and plan for maintenance and reduce repair time, which in turn ensures the availability of operated systems. In this context, supervised Machine Learning (ML) algorithms are undergoing extensive development and their performances are analyzed in terms of different metrics including computation time detection accuracy, precision, recall, and F1-score in addition to False Alarm and missed detection rates. However, such algorithms generally require the extraction of significant features to handle irrelevant and redundant features while reducing the computational burden. Considering that different dimensionality reduction methods directly impact the FDD performances, this paper presents a novel Data Dimensionality Reduction Strategy (DDRS) based on a proposed algorithm for Information Gain (IG) score optimal threshold determination. This strategy also relies on Principal Component Analysis (PCA) to obtain the optimal subset which represents the lowest dimensional feature in the training and testing stages of different supervised ML algorithms. FDD performances are then evaluated by using faulty and non-faulty datasets acquired experimentally from a Grid-connected PV system (GPVS) emulator. The experimental results show the efficiency of the proposed strategy and its ability to improve the FDD based on supervised ML algorithms.

A Novel PCB Defect Detection Method Based on Digital Image Processing

Printed circuit board (PCB) has a wide range of applications, and its automated quality inspection is a vital part of economic production. The time overhead of the current PCB defect detection method will increase with the increase in the number of detection targets. To ensure the stability of detection performance, this paper proposes a novel PCB detection method based on color threshold segmentation. Experiments show that this method can effectively detect and analyse various information about solder pads and solder pastes, among which the detection accuracy rate of solder pads is 99.4%, the missed detection rate is 0.4%, and the detection accuracy rate of solder paste is 99.3%, The missed detection rate is 0.03%. At the same time, the method in this paper has better robustness and stability than other PCB defect detection methods, and can better meet the needs of industrial production.

A Spatially Self-Adaptive Multiparametric Anomaly Identification Scheme Based on Global Strong Earthquakes

Earthquake forecasting aims to determine the likelihood of a damaging earthquake occurring in a particular area within a period of days to months. This provides ample preparation time for potential seismic hazards, resulting in significant socioeconomic benefits. Surface and atmospheric parameters derived from satellite thermal infrared observations have been utilized to identify pre-earthquake anomalies that may serve as potential precursors for earthquake forecasting. However, the correlation between these anomalies and impending earthquakes remains a significant challenge due to high false alarm and missed detection rates. To address this issue, we propose a spatially self-adaptive multiparametric anomaly identification scheme based on global strong earthquakes to establish the optimal recognition criteria. Each optimal parameter exhibits significant spatial variability within the seismically active region and indicates transient and subtle anomaly signals with a limited frequency of occurrences (<10 for most regions). In comparison to the fixed criterion for identifying anomalies, this new scheme significantly improves the positive Matthew’s correlation coefficient (MCC) values from ~0.03 to 0.122–0.152. Additionally, we have developed a multi-parameter anomaly synthesis method based on the best MCC value of each parameter anomaly. On average, the MCC increased from 0.143 to 0.186, and there are now more earthquake-prone regions with MCC values > 0.5. Our research emphasizes the critical importance of a multiparametric system in earthquake forecasting, where each geophysical parameter can be assigned a distinct weight, and the findings specifically identify OLR, including all-sky and clear-sky ones, as the most influential parameter on a global scale, highlighting the potential significance of OLR anomalies for seismic forecasting. Encouraging results imply the effectiveness of utilizing multiparametric anomalies and provide some confidence in advancing our knowledge of operational earthquake forecasting with a more quantitative approach.

Surface Defect Detection for Automated Tape Laying and Winding Based on Improved YOLOv5.

To address the issues of low detection accuracy, slow detection speed, high missed detection rate, and high false detection rate in the detection of surface defects on pre-impregnated composite materials during the automated tape laying and winding process, an improved YOLOv5 (You Only Look Once version 5) algorithm model was proposed to achieve the high-precision, real-time detection of surface defects. By leveraging this improvement, the necessity for frequent manual interventions, inspection interventions, and subsequent rework during the automated lay-up process of composite materials can be significantly reduced. Firstly, to improve the detection accuracy, an attention mechanism called "CA (coordinate attention)" was introduced to enhance the feature extraction ability, and a Separate CA structure was used to improve the detection speed. Secondly, we used an improved loss function "SIoU (SCYLLA-Intersection over Union) loss" to replace the original "CIoU (Complete-Intersection over Union) loss", which introduced an angle loss as a penalty term to consider the directional factor and improve the stability of the target box regression. Finally, Soft-SIoU-NMS was used to replace the original NMS (non-maximum suppression) of YOLOv5 to improve the detection of overlapping defects. The results showed that the improved model had a good detection performance for surface defects on pre-impregnated composite materials during the automated tape laying and winding process. The FPS (frames per second) increased from 66.7 to 72.1, and the mAP (mean average precision) of the test set increased from 92.6% to 97.2%. These improvements ensured that the detection accuracy, as measured by the mAP, surpassed 95%, while maintaining a detection speed of over 70 FPS, thereby meeting the requirements for real-time online detection.

Machine Vision Algorithm for Identifying Packaging Components of HN-3 Arterial Blood Sample Collector

The arterial blood sample collector produced in large quantities often fails to meet the requirements due to missing components in the packaging bag, and traditional manual detection methods are both inefficient and inaccurate. To solve this problem, a PyCharm-integrated development environment was used to study image processing and recognition algorithms for identifying components inside the packaging bag of the HN-3 arterial blood sample collector. The machine vision system was used to capture images of the packaging bags of the HN-3 Arterial blood sample collector. Template matching was employed to extract the packaging ROI, and the threshold segmentation method in the HSV color model was used to extract material features based on the packaging ROI. Morphological processing algorithms such as dilation or erosion were used to enhance the connectivity of the extracted features. The existence of components was determined by setting thresholds for the connected domain area or length. The results of the recognition experiment show that the false detection rate is 0.2%, the missed detection rate is 0%, and the average image processing time per product is no more than 39 ms. Compared with manual recognition methods, the efficiency and accuracy have been improved by 36.5 times and 2.3%, respectively. The experimental results confirm the effectiveness of the image processing algorithm.

A Multi-Target Detection Method Based on Improved U-Net for UWB MIMO Through-Wall Radar

Ultra-wideband (UWB) multiple-input multiple-output (MIMO) through-wall radar is widely used in through-wall human target detection for its good penetration characteristics and resolution. However, in actual detection scenarios, weak target masking and adjacent target unresolving will occur in through-wall imaging due to factors such as resolution limitations and differences in human reflectance, which will reduce the probability of target detection. An improved U-Net model is proposed in this paper to improve the detection probability of through-wall targets. In the proposed detection method, a ResNet module and a squeeze-and-excitation (SE) module are integrated in the traditional U-Net model. The ResNet module can reduce the difficulty of feature learning and improve the accuracy of detection. The SE module allows the network to perform feature recalibration and learn to use global information to emphasize useful features selectively and suppress less useful features. The effectiveness of the proposed method is verified via simulations and experiments. Compared with the order statistics constant false alarm rate (OS-CFAR), the fully convolutional networks (FCN) and the traditional U-Net, the proposed method can detect through-wall weak targets and adjacent unresolving targets effectively. The detection precision of the through-wall target is improved, and the missed detection rate is minimized.

Side-scan sonar underwater target segmentation using the BHP-UNet

Although target detection algorithms based on deep learning have achieved good results in the detection of side-scan sonar underwater targets, their false and missed detection rates are high for multiple densely arranged and overlapping underwater targets. To address this problem, a side-scan sonar underwater target segmentation model based on the blended hybrid dilated convolution and pyramid split attention UNet (BHP-UNet) algorithm is proposed in this paper. First, the blended hybrid dilated convolution module is adopted to improve the ability of the model to learn deep semantics and shallow features while improving the receptive field. Second, the pyramid split attention module is introduced to establish a long-term dependency between global and local information while processing multi-scale spatial features. Three sets of experimental results show that the BHP-UNet model proposed in this paper has better segmentation performance than the conventional fully convolutional network, UNet, and DeepLabv3+ models, and it is able to segment dense and overlapping targets to a certain extent. The proposed model will have significance as a guide for practical applications.

Rebar Cross-section Detection Based on Improved YOLOv5s Algorithm

Rebar is an indispensable material in the construction industry and the counting of rebar is a very important part of the production, transportation, and use process. Due to the problems of dense cross-sections and mutual adhesion of cross-sections boundaries for bundles of rebar, most of the rebar is still counted manually. To address the above problems, this paper proposes an improved YOLOv5s-based rebar cross-section detection method, which aims to solve the problems of missed and false detection in dense small object detection. The K-means algorithm is used to re-cluster the anchor box size; the ECANet (Efficient Channel Attention Net) module is integrated with the backbone network to adjust the attention weight of the feature map and improve the feature extraction ability of the network; To address the problem that small object with a scale smaller than 8×8 in the network cannot be detected, a new detection layer scale is redesigned by fusing the high-level semantic information with the low-level features to reduce the network's miss detection rate for small object. Experiments were conducted on a homemade rebar cross-section dataset with mAP0.5 of 94.3%, which is 6.2% improvement compared with the baseline model YOLOv5s. The improved network was able to better identify the cross-sectional features of the rebar, thus significantly reducing the leakage rate in rebar counting detection.

Ship Detection in Low-Quality SAR Images via an Unsupervised Domain Adaption Method

Ship detection in low-quality Synthetic Aperture Radar (SAR) images poses a persistent challenge. Noise signals in complex environments disrupt imaging conditions, hindering SAR systems from acquiring precise target information, thereby significantly compromising the performance of detectors. Some methods mitigate interference via denoising techniques, while others introduce noise using classical methods to learn target features in the presence of noise. This conundrum is regarded as a cross-domain problem in this paper; a ship detection method in low-quality images is proposed to learn features of targets and shrink serious deterioration of detection performance by utilizing Generative Adversarial Networks (GANs). First, an image-to-image translation task is implemented using CycleGAN to generate low-quality SAR images with complex interference from the source domain to the target domain. Second, with the annotation inheritance, these generated SAR images participate in a training process to improve the detection accuracy and model robustness. Multiple experiments indicate that the proposed method conspicuously improves the detection performance and efficaciously reduces the missed detection rate in the SAR ship detection task. This cross-domain approach achieved outstanding improvements in the form of 11.0% mAP and 3.22% mAP on the GaoFen-3 ship dataset and SRSSD-V1.0, respectively. In addition, the characteristics and potentials of near-shore and off-shore SAR image reconstruction with style transfer based on Generative Adversarial Networks were explored and analyzed in this work.

Satellite Video Moving Vehicle Detection and Tracking Based on Spatiotemporal Characteristics

The complex backgrounds of satellite videos and serious interference from noise and pseudo-motion targets make it difficult to detect and track moving vehicles. Recently, researchers have proposed road-based constraints to remove background interference and achieve highly accurate detection and tracking. However, existing methods for constructing road constraints suffer from poor stability, low arithmetic performance, leakage, and error detection. In response, this study proposes a method for detecting and tracking moving vehicles in satellite videos based on the constraints from spatiotemporal characteristics (DTSTC), fusing road masks from the spatial domain with motion heat maps from the temporal domain. The detection precision is enhanced by increasing the contrast in the constrained area to accurately detect moving vehicles. Vehicle tracking is achieved by completing an inter-frame vehicle association using position and historical movement information. The method was tested at various stages, and the results show that the proposed method outperformed the traditional method in constructing constraints, correct detection rate, false detection rate, and missed detection rate. The tracking phase performed well in identity retention capability and tracking accuracy. Therefore, DTSTC is robust for detecting moving vehicles in satellite videos.

Improved YOLOv5l-based Detection of Surface Defects in Hot Rolled Steel Strips

To address the problems of complex background, different sizes and easy to miss and mis-detect in the detection of surface defects in hot-rolled strip, an improved YOLOv5l-based method for detecting surface defects in hot-rolled strip is proposed. Firstly, by adding the SimAM attention mechanism module to the aggregation network, the important information is focused with high weights to improve the recall rate of the original algorithm; secondly, by replacing all C3 modules in the YOLOv5l structure with C2F, a richer gradient of information flow is obtained to improve the accuracy rate of the original algorithm. The experimental results show that the average detection accuracy using the improved YOLOv5l improves by 5.3% and the accuracy rate by 8.3% compared to the original network, resulting in higher detection accuracy and lower error and miss detection rates, meeting the requirements of hot-rolled strip steel inspection in industrial manufacturing.

Intelligent Detection Method for Concrete Dam Surface Cracks Based on Two-Stage Transfer Learning

The timely identification and detection of surface cracks in concrete dams, an important public safety infrastructure, is of great significance in predicting engineering hazards and ensuring dam safety. Due to their low efficiency and accuracy, manual detection methods are gradually being replaced by computer vision techniques, and deep learning semantic segmentation methods have higher accuracy and robustness than traditional image methods. However, the lack of data images and insufficient detection performance remain challenges in concrete dam surface crack detection scenarios. Therefore, this paper proposes an intelligent detection method for concrete dam surface cracks based on two-stage transfer learning. First, relevant domain knowledge is transferred to the target domain using two-stage transfer learning, cross-domain and intradomain learning, allowing the model to be fully trained with a small dataset. Second, the segmentation capability is enhanced by using residual network 50 (ResNet50) as a UNet model feature extraction network to enhance crack feature information extraction. Finally, multilayer parallel residual attention (MPR) is integrated into its jump connection path to improve the focus on critical information for clearer fracture edge segmentation. The results show that the proposed method achieves optimal mIoU and mPA of 88.3% and 92.7%, respectively, among many advanced semantic segmentation models. Compared with the benchmark UNet model, the proposed method improves mIoU and mPA by 4.6% and 3.2%, respectively, reduces FLOPs by 36.7%, improves inference speed by 48.9%, verifies its better segmentation performance on dam face crack images with a low fine crack miss detection rate and clear crack edge segmentation, and achieves an accuracy of over 85.7% in crack area prediction. In summary, the proposed method has higher efficiency and accuracy in concrete dam face crack detection, with greater robustness, and can provide a better alternative or complementary approach to dam safety inspections than the benchmark UNet model.

Multi sensor moving image fusion analysis algorithm on the basis of neural network technology

Image fusion can extract the useful information of each channel about the same target to the maximum extent, and get high quality image. However, in this process, the image quality may be affected by noise and reduced. To reduce the image noise’s influence on the image fusion effect as well as improve the image fusion quality, a multi sensor moving image fusion analysis algorithm on the basis of neural network technology is proposed. This study designed a window adaptive strategy, use the probability density function, and built an impulse noise model, and use this model to divide each pixel in the image into noise points or signal points to obtain image impulse noise detection results, and use bilateral filtering algorithm to achieve image denoising processing; The fruit fly optimization algorithm is adopted to detect the edge of the multi sensor moving image, extract the image’s main edge points, and remove the detail edge points and noise points; nonlinear convolutional layer is used to replace most fusion layers to improve the dense network model, and the cross-entropy loss is used as the loss function in training the network, then use guided filters to generate guide maps, and generate final fusion images. According to experimental results, the noise detection method in this paper can also maintain 79.21% non-noise extraction rate under the noise density of 0.7. The highest correlation coefficient between the proposed algorithm and the standard image is 37.41. Its peak signal-to-noise ratio is as low as 0.09 and as high as 0.52. It has a minimum root mean square error of 8.52. The above values are better than other measured methods, and its edge miss rate can be as low as 1%, the image resolution is higher. It can be seen that its image denoising effect is better. Image denoising effect, and low edge missed detection rate, which effectively improves the effect of image fusion.

LSTM-NV: A combined scheme against selective forwarding attack in event-driven wireless sensor networks under harsh environments

Wireless Sensor Networks (WSNs) are susceptible to selective forwarding attacks, which can lead to reduced network efficiency and compromise the integrity of transmitted data. The indistinguishability between malicious behavior and normal packet loss in harsh environments exacerbates the challenge. To address this, in this paper, we provide a combined detection scheme called LSTM-NV, consisting of a training stage and a detecting stage. During the training stage, we integrate variational mode decomposition (VMD) with a long short term memory (LSTM) model to learn normal nodes’ forwarding behavior and then predict errors for each node. During the detecting stage, dynamic thresholds are determined to identify local anomaly points and a novel neighbor voting method is employed to differentiate between malicious and normal nodes. Our scheme demonstrates superior performance with a low average missed detection rate (MDR) of 0.6% and a low average false detection rate (FDR) of 3.3% compared to other effective methods, while also offering lower detection algorithmic complexity.

A Novel Deep Learning Network with Deformable Convolution and Attention Mechanisms for Complex Scenes Ship Detection in SAR Images

Synthetic aperture radar (SAR) can detect objects in various climate and weather conditions. Therefore, SAR images are widely used for maritime object detection in applications such as maritime transportation safety and fishery law enforcement. However, nearshore ship targets in SAR images are often affected by background clutter, resulting in a low detection rate, high false alarm rate, and high missed detection rate, especially for small-scale ship targets. To address this problem, in this paper, we propose a novel deep learning network with deformable convolution and attention mechanisms to improve the Feature Pyramid Network (FPN) model for nearshore ship target detection in SAR images with complex backgrounds. The proposed model uses a deformable convolutional neural network in the feature extraction network to adapt the convolution position to the target sampling point, enhancing the feature extraction ability of the target, and improving the detection rate of the ship target against the complex background. Moreover, this model uses a channel attention mechanism to capture the feature dependencies between different channel graphs in the feature extraction network and reduce the false detection rate. The designed experiments on a public SAR image ship dataset show that our model achieves 87.9% detection accuracy for complex scenes and 95.1% detection accuracy for small-scale ship targets. A quantitative comparison of the proposed model with several classical and recently developed deep learning models on the same SAR images dataset demonstrated the superior performance of the proposed method over other models.

Dynamic obstacle detection method based on U–V disparity and residual optical flow for autonomous driving

As a core step of obstacle avoidance and path planning, dynamic obstacle detection is critical for autonomous driving. This study aimed to propose a dynamic obstacle detection method based on U–V disparity and residual optical flow for autonomous driving. First, a drivable area of an unmanned vehicle was detected using U–V disparity images. Then, obstacles in the drivable area were detected using U–V disparity images and the geometric relationship between obstacle size and its disparity. Finally, the motion likelihood of each obstacle was estimated by compensating the camera ego-motion. The innovation of the proposed method was that the searching range of the moving obstacles was greatly narrowed by detecting the obstacles in the drivable area, which greatly improved not only the moving obstacle detection efficiency but also the detection accuracy. Datasets from the KITTI benchmark and our self-acquired campus scene data were chosen as testing samples. The experimental results showed that our method could achieve high detection precision, low missed detection rate and less time consumption.