Missed Detection Rate Research Articles

FRCNN-AA-CIF: An automatic detection model of colon polyps based on attention awareness and context information fusion

It is noted that the foreground and background of the polyp images detected under colonoscopy are not highly differentiated, and the feature map extracted by common deep learning object detection models keep getting smaller as the number of networks increases. Therefore, these models tend to ignore the details in pictures, resulting in a high polyp missed detection rate. To reduce the missed detection rate, this paper proposes an automatic detection model of colon polyps based on attention awareness and context information fusion (FRCNN-AA-CIF) based on a two-stage object detection model Faster Region-Convolutional Neural Network (FR-CNN). First, since the addition of attention awareness can make the feature extraction network pay more attention to polyp features, we propose an attention awareness module based on Squeeze-and-Excitation Network (SENet) and Efficient Channel Attention Module (ECA-Net) and add it after each block of the backbone network. Specifically, we first use the 1*1 convolution of ECA-Net to extract local cross-channel information and then use the two fully connected layers of SENet to reduce and increase the dimension, to filter out the channels that are more useful for feature learning. Further, because of the presence of air bubbles, impurities, inflammation, and accumulation of digestive matter around polyps, we used context information around polyps to enhance the focus on polyp features. In particular, after the network extracts the region of interest, we fuse the region of interest with its context information to improve the detection rate of polyps. The proposed model was tested on the colonoscopy dataset provided by Huashan Hospital. Numerical experiments show that FRCNN-AA-CIF has the highest detection accuracy (mAP of 0.817), the lowest missed detection rate of 4.22%, and the best classification effect (AUC of 95.98%). Its mAP increased by 3.3%, MDR decreased by 1.97%, and AUC increased by 1.8%. Compared with other object detection models, FRCNN-AA-CIF has significantly improved recognition accuracy and reduced missed detection rate.

Endoscopic Screening for Missed Lesions of Synchronous Multiple Early Gastric Cancer during Endoscopic Submucosal Dissection.

To evaluate the value of endoscopic screening during endoscopic submucosal dissection (ESD) in the detection of synchronous multiple early gastric cancer (SMEGC) and the risk factors for missed diagnosis of SMEGC. We conducted gastric endoscopic screening during ESD operation in 271 patients with early gastric cancer (EGC) referred for ESD, and endoscopic follow-up within 1 year after the operation. The detection and characteristics of SMEGC were analyzed in three stages: before ESD, during ESD operation, and within 1 year after ESD. SMEGC was detected in 37 of 271 patients (13.6%). Among them, 21 patients with SMEGC (56.8%) were diagnosed before ESD, 9 (24.3%) were diagnosed with SMEGC by endoscopic screening during ESD operation, and 7 (18.9%) were found to have EGC lesions in the stomach during postoperative endoscopic follow-up within 1 year. The preoperative missed detection rate of SMEGC was 43.2%, and the rate of missed detection could be reduced by 24.3% (9/37) with endoscopic screening during ESD operation. Missed SMEGC lesions were more common in flat or depressed type and smaller in size than the lesions found before ESD. The presence of severe atrophic gastritis and age ≥60 years were significantly correlated with SMEGC (P < 0.05), while multivariate analysis showed that age ≥60 years was an independent risk factor (OR = 2.63, P < 0.05) for SMEGC. SMEGC lesions are apt to be missed endoscopically. Special attention should be paid to small, depressed, or flat lesions in detecting SMEGC, especially in elderly patients or (and) patients with severe atrophic gastritis. Endoscopic screening during ESD operation can effectively reduce the missed diagnosis rate of SMEGC.

Mathematical modeling analysis of potential attack detection in topology network based on convolutional neural network

New network attack platforms such as personal to personal botnets pose a great threat to cyberspace, but there is no corresponding detection method to detect them. In order to improve the security of topological networks, this research designs a mathematical modeling analysis method for potential attack detection based on convolutional neural networks. This method determines the potential attack risk assessment function through the feature extraction of vulnerable areas in network topology and the probability model of potential attacks, and then detects potential attacks by means of convolutional neural network data modeling. The experimental results show that the false detection rate and missed detection rate of the three methods for potential attacks are lower than 9% and 8% respectively, but the false detection rate and missed detection rate of the method given in the study are the lowest, and can always be kept below 5%. At the same time, the detection time of potential attacks of this method is shorter than that of the other two detection methods. The detection of potential attacks provides a technical guarantee for the safe operation of the network.

Extracapsular extension of transitional zone prostate cancer miss-detected by multiparametric magnetic resonance imaging.

To demonstrate the importance of extracapsular extension (ECE) of transitional zone (TZ) prostate cancer (PCa), examine the causes of its missed detection by Mp-MRI, and develop a new predictive model by integrating multi-level clinical variables. This retrospective study included 304 patients who underwent laparoscopic radical prostatectomy after 12 + X needle transperineal transrectal ultrasound (TRUS)-MRI-guided targeted prostate biopsy from 2018 to 2021 in our center was performed. In this study, the incidence rates of ECE were similar in patients with MRI lesions in the peripheral zone (PZ) and TZ (P = 0.66). However, the missed detection rate was higher in patients with TZ lesions than in those with PZ lesions (P < 0.05). These missed detections result in a higher positive surgical margin rate (P < 0.05). In patients with TZ lesions, detected MP-MRI ECE may have grey areas: the longest diameters of the MRI lesions were 16.5-23.5mm; MRI lesion volumes were 0.63-2.51ml; MRI lesion volume ratios were 2.75-8.86%; PSA were 13.85-23.05ng/ml. LASSO regression was used to construct a clinical prediction model for predicting the risk of ECE in TZ lesions from the perspective of MRI and clinical features, including four variables: the longest diameter of MRI lesions, TZ pseudocapsule invasion, ISUP grading of biopsy pathology, and number of positive biopsy needles. Patients with MRI lesions in the TZ have the same incidence of ECE as those with lesions in the PZ, but a higher missed detection rate.

Extracting Traffic Signage by Combining Point Clouds and Images.

Recognizing traffic signs is key to achieving safe automatic driving. With the decreasing cost of LiDAR, the accurate extraction of traffic signs using point cloud data has received wide attention. In this study, we propose combining point cloud and image traffic sign extraction: firstly, we use the improved YoloV3 model to detect traffic signs in panoramic images. The specific improvement is that the convolution block attention module is added to the algorithm framework, the traditional K-means clustering algorithm is improved, and Focal Loss is introduced as the loss function. It shows higher accuracy on the TT100K dataset, with a 1.4% improvement in accuracy compared to the previous YoloV3. Then, the point cloud of the area where the traffic sign is located is extracted by combining the image detection results. On this basis, the outline of the traffic sign is accurately extracted using the reflection intensity, spatial geometry and other information. Compared with the traditional method, the proposed method can effectively reduce the missed detection rate, narrow the range of point cloud, and improve the detection accuracy by 10.2%.

YOLOv7-RAR for Urban Vehicle Detection.

Aiming at the problems of high missed detection rates of the YOLOv7 algorithm for vehicle detection on urban roads, weak perception of small targets in perspective, and insufficient feature extraction, the YOLOv7-RAR recognition algorithm is proposed. The algorithm is improved from the following three directions based on YOLOv7. Firstly, in view of the insufficient nonlinear feature fusion of the original backbone network, the Res3Unit structure is used to reconstruct the backbone network of YOLOv7 to improve the ability of the network model architecture to obtain more nonlinear features. Secondly, in view of the problem that there are many interference backgrounds in urban roads and that the original network is weak in positioning targets such as vehicles, a plug-and-play hybrid attention mechanism module, ACmix, is added after the SPPCSPC layer of the backbone network to enhance the network's attention to vehicles and reduce the interference of other targets. Finally, aiming at the problem that the receptive field of the original network Narrows, with the deepening of the network model, leads to a high miss rate of small targets, the Gaussian receptive field scheme used in the RFLA (Gaussian-receptive-field-based label assignment) module is used at the connection between the feature fusion area and the detection head to improve the receptive field of the network model for small objects in the image. Combining the three improvement measures, the first letter of the name of each improvement measure is selected, and the improved algorithm is named the YOLOv7-RAR algorithm. Experiments show that on urban roads with crowded vehicles and different weather patterns, the average detection accuracy of the YOLOv7-RAR algorithm reaches 95.1%, which is 2.4% higher than that of the original algorithm; the AP50:90 performance is 12.6% higher than that of the original algorithm. The running speed of the YOLOv7-RAR algorithm reaches 96 FPS, which meets the real-time requirements of vehicle detection; hence, the algorithm can be better applied to vehicle detection.

Improved traffic sign recognition algorithm based on YOLOv4-tiny

This study offers an enhanced yolov4-tiny traffic sign identification method for easy deployment on mobile or embedded devices to address the difficulties of a high number of parameters, low recognition accuracy, and poor real-time performance of traffic sign recognition models in complex scenarios. The yolov4-tiny network serves as the model’s foundation. To begin, Octave Convolution is incorporated into the backbone network to eliminate low-frequency feature redundancy, lowering the number of parameters in the model and enhancing computational efficiency. Second, the convolutional block attention module is employed to improve the recognition accuracy of small and medium-sized targets by strengthening the weights of traffic sign regions and suppressing the weights of invalid features. Finally, in the feature fusion stage, the Feature Pyramid Networks structure is replaced with the Simplified Path Aggregation Network structure to improve the fusing of shallow feature information with deep semantic knowledge and lower the miss detection rate even more On the TT100K data set as well as on CCTSDB dataset, the experimental results suggest that our technique can achieve good recognition performance. With a 16MB model size, our solution improves the mean average precision by 3.5 percent and the Frame Per Second by 12.5 f/s when compared to the yolov4-tiny algorithm. Our method outperforms yolov4-tiny in terms of recognition accuracy and detection speed, and it can easily meet the real-time requirements for traffic sign recognition.

Misbehavior Detection and Identification in DF Multi-Relay VANETs

In this letter, we study the multi-relay misbehavior detection and identification problem in decode-and-forward (DF) vehicular ad hoc networks (VANETs). To simplify detector design, we present a novel metric, hypothesis variance ratio (HVR), by which the closed-form expressions of the false-alarm rate, miss-detection rate, weighted detection error probability (WDEP) and the optimal threshold to minimize the WDEP are derived. Furthermore, the monotonicity of the above metrics with respect to the HVR is proved. Based on the above theories and the sparsity of relay misbehaviors, we develop a pre-detection scheme to reduce computation latency. The maximum HVR based sufficient statistic is constructed to achieve high latency reduction rates. It is demonstrated that the proposed scheme outperforms the average-power-Neyman-Pearson scheme in terms of the WDEP.

SAR image near-shore ship target detection method in complex background

ABSTRACT Due to background clutter in synthetic aperture radar (SAR) images, the detection of dense ship targets suffers from a low detection rate, high false alarm rate, and high missed detection rate. To address this issue, an FSM-DFF-YOLOv5+Confluence algorithm is proposed in this paper for the detection of near-shore ship targets in SAR images with complex backgrounds. First, based on the YOLOv5 target detection algorithm, two improvements are made in the feature extraction network: feature refinement and multi-feature fusion; in the feature extraction network, deformable convolutional neural networks are adopted to change the position of the target sampling points of the convolution to improve the feature extraction capability of the target and the detection rate of ship targets in SAR images with a complex background; in the multi-feature fusion network structure, cascading and parallel pyramids are used in the multi-feature fusion network to realize feature fusion at different levels; the visual perceptual field of feature extraction is expanded by using null convolution to enhance the adaptability of the network to detect near-shore multi-scale ship targets with complex backgrounds and reduce the false alarm rate of ship target detection in SAR images with complex environments. In this way, the DFF-YOLOv5 near-shore ship target detection algorithm is established. Meanwhile, to address the problem of missed detection in near-shore dense ship target detection, this paper adds rectangular convolution kernels to the convolution of the feature extraction network to better realize the feature extraction of dense ship targets in SAR images with complex backgrounds. Besides, the Confluence algorithm instead of non-maximum suppression is used in the prediction stage. Through experiments on the constructed complex background near-shore ship detection dataset, it is indicated that the average accuracy of the FSM-DFF-YOLOv5+Confluence detection algorithm reaches 88.96%, and the recall rate reaches 88.80%.

Fast plug-in capacitors polarity detection with morphology and SVM fusion method in automatic optical inspection system

Defect detection is a critical element in the PCB manufacturing process. Different from surface mount PCB, the components on the plug-in PCB are usually installed manually, resulting in significant errors. We make contributions in the following two aspects: (1) a framework and measurement method of a light source and make a cheap and efficient lighting system; (2) a fusion algorithm based on machine learning and morphology for polarity detection of plug-in capacitors. The capacitor is detected using SVM and fused with the polar coordinate expansion method. The AOI system and the proposed fusion algorithm have been applied to the production line, with an accuracy of \(99.73\%\) and a missed detection rate \(0.12\%\).

Crowd counting using deep learning based head detection

Scale invariance and high miss detection rates for small objects are some of the challenging issues for object detection and often lead to inaccurate results. This research aims to provide an accurate detection model for crowd counting by focusing on human head detection from natural scenes acquired from publicly available datasets of Casablanca, Hollywood-Heads and Scut-head. In this study, we tuned a yolov5, a deep convolutional neural network (CNN) based object detection architecture, and then evaluated the model using mean average precision (mAP) score, precision, and recall. The transfer learning approach is used for fine-tuning the architecture. Training on one dataset and testing the model on another leads to inaccurate results due to different types of heads in different datasets. Another main contribution of our research is combining the three datasets into a single dataset, including every kind of head that is medium, large and small. From the experimental results, it can be seen that this yolov5 architecture showed significant improvements in small head detections in crowded scenes as compared to the other baseline approaches, such as the Faster R-CNN and VGG-16-based SSD MultiBox Detector.

Mask Detection Method Based on YOLO-GBC Network

For the problems of inaccurate recognition and the high missed detection rate of existing mask detection algorithms in actual scenes, a novel mask detection algorithm based on the YOLO-GBC network is proposed. Specifically, in the backbone network part, the global attention mechanism (GAM) is integrated to improve the ability to extract key information through cross-latitude information interaction. The cross-layer cascade method is adopted to improve the feature pyramid structure to achieve effective bidirectional cross-scale connection and weighted feature fusion. The sampling method of content-aware reassembly of features (CARAFE) is integrated into the feature pyramid network to fully retain the semantic information and global features of the feature map. NMS is replaced with Soft-NMS to improve model prediction frame accuracy by confidence decay method. The experimental results show that the average accuracy (mAP) of the YOLO-GBC reached 91.2% in the mask detection data set, which is 2.3% higher than the baseline YOLOv5, and the detection speed reached 64FPS. The accuracy and recall have also been improved to varying degrees, increasing the detection task of correctly wearing masks.

Mathematical modelling of abnormal account detection on social media platform based on improved edge weight

In order to overcome the problems of traditional methods such as large time consumption, high missed detection rate and low recall rate of detection results, a mathematical modelling method of abnormal account detection on social media platform based on improved edge weight was proposed. The social media platform is regarded as a directed social graph, the node states in the social graph are judged, and the edge weight of the social graph is used to calculate the edge potential. First, take the improved edge weight processing results as the operation constraints of the model. Then, build the abnormal account detection mathematical model. Finally, input the user account information into the model, and output the abnormal account detection results. The experimental results show that the maximum detection time consumption of the proposed method is only 0.88 min, the highest missed detection rate is only 2.9%, and the lowest recall rate can reach 98.49%.

Particle Filter-Based Fault Detection for Toolface Measurement of Rotary Steerable Systems

In this paper, the problem of accelerometer fault detection is studied for the rotary steerable drilling tool system (RSDTS) which is modeled by a nonlinear stochastic system. Owing to the strong vibration in the drilling process, RSDTSs are inevitably influenced by strong measurement noise, which brings in substantial difficulties in the fault detection. Firstly, a modified particle filtering algorithm is proposed to improve the accuracy of state estimation. Next, by means of the moving average method, the residual which approximatively obeys the χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> distribution is designed when the estimation error is small enough. According to the property of the χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> distribution, the window length of the moving average method is determined. Moreover, a fault detection algorithm is presented, and false alarm rate and missed detection rate are quantitatively analyzed for nonlinear stochastic systems. Finally, the effectiveness of the developed fault detection method is verified by simulation and experiment on the prototype of the RSDTS.

Drift Detection Method Using Distance Measures and Windowing Schemes for Sentiment Classification

Textual data streams have been extensively used in practical applications where consumers of online products have expressed their views regarding online products. Due to changes in data distribution, commonly referred to as concept drift, mining this data stream is a challenging problem for researchers. The majority of the existing drift detection techniques are based on classification errors, which have higher probabilities of false-positive or missed detections. To improve classification accuracy, there is a need to develop more intuitive detection techniques that can identify a great number of drifts in the data streams. This paper presents an adaptive unsupervised learning technique, an ensemble classifier based on drift detection for opinion mining and sentiment classification. To improve classification performance, this approach uses four different dissimilarity measures to determine the degree of concept drifts in the data stream. Whenever a drift is detected, the proposed method builds and adds a new classifier to the ensemble. To add a new classifier, the total number of classifiers in the ensemble is first checked if the limit is exceeded before the classifier with the least weight is removed from the ensemble. To this end, a weighting mechanism is used to calculate the weight of each classifier, which decides the contribution of each classifier in the final classification results. Several experiments were conducted on real-world datasets and the results were evaluated on the false positive rate, miss detection rate, and accuracy measures. The proposed method is also compared with the state-of-the-art methods, which include DDM, EDDM, and PageHinkley with support vector machine (SVM) and Naïve Bayes classifiers that are frequently used in concept drift detection studies. In all cases, the results show the efficiency of our proposed method.

Network Security Intrusion Detection Methods Combining Optimization Algorithms and Neural Networks

Due to the diverse and volatile nature of today's network attacks, it is difficult to achieve satisfactory results using a single detection method. This project plans to study the network intrusion detection algorithm based on Semi-Supervised Learning and active learning to improve its accuracy and effectively intercept various network attacks. First, network attack data is collected, attack characteristics are extracted, and Semi-Supervised Learning method is used to cluster attack data. Secondly, by studying the clustering data, a network intrusion detection classifier based on BP neural network was constructed, and the construction process of the classifier was improved. On this basis, the effectiveness of the algorithm was verified through simulation experiments using standard datasets. The experimental results show that the combination rate of the optimized algorithm for network intrusion detection and neural network methods is over 95%, which can meet the actual accuracy requirements of network security protection. This is because the method proposed by the author combines semi supervised technology with BP neural network, which can effectively identify different types of network intrusion behavior, thereby reducing the missed detection rate and false alarm rate of network intrusion detection, and has significant advantages.

Energy consumption mode identification and monitoring method of process industry system under unstable working conditions

Process industry systems under unstable working conditions are prone to potential anomalies, deviating from the original transition trajectory, and taking longer than expected to return to stability due to persistent disturbances from uncertainties and experience-based regulation errors. The energy waste caused by this situation has not received sufficient attention, and cannot be addressed by existing energy consumption monitoring methods. Herein, an energy consumption mode (ECM) identification and monitoring method under unstable working conditions is proposed, consisting of ECM identification model and multi-mode dynamic monitoring model, focusing on the variation rules of the correlation between energy consumption and other states of the system. In the ECM identification stage, the ECM correlation parameters that reflect the comprehensive production information are selected. Then, given the transfer characteristics of ECM, a Hidden Semi-Markov Model (HSMM) is constructed to fit the migration between modes and the duration within modes. The Variational Bayesian Gaussian Mixture Model is introduced to improve the HSMM, which solves the problem of lacking prior knowledge of ECM and achieves the automatic classification and online identification of ECM. In the dynamic monitoring stage of multi-ECMs, a series of dynamic kernel principle component analysis models are established, and the corresponding monitoring thresholds are set for each ECM. By calculating the maximum of the posteriori probability and the mode thresholds, the ECMs under unstable conditions can be accurately identified and automatically monitored. Compared with previous methods, the proposed method reduces the false detection rate and missed detection rate of abnormal ECM identification to 1.04% and 1.31% in the actual slag grinding production process, which proves its effectiveness.

Improved Traffic Sign Recognition Algorithm Based on YOLOV4-Tiny

We propose an improved YOLOV4-tiny traffic sign recognition algorithm for easy deployment on mobile or embedded devices to address the problems of a large number of parameters, low recognition accuracy in complex scenarios. The model uses the YOLOV4-tiny network as the basic framework. First, Octave Convolution is introduced into the backbone network to reduce the redundancy of low-frequency features. Second, the convolutional block attention module is used to strengthen the weights of traffic sign regions and reduce the weights of invalid features. Finally, the Feature Pyramid Network structure is replaced by the Simplified Path Aggregation Network structure in the feature fusion stage to enhance the feature information and further reduce the miss detection rate. The experiment proves that our method outperforms YOLOV4-tiny in recognition accuracy and detection speed on the TT100[Formula: see text]K dataset, and can easily meet the requirements of traffic sign recognition.

An Extendable Gaussian Mixture Model for Lane-Based Queue Length Estimation Based on License Plate Recognition Data

Most existing studies on queue length estimation based on license plate recognition (LPR) data require multisection LPR data. Studies based on single-section LPR data cannot ensure the accuracy and stability of the estimates when missed detections occur, which greatly limits the practicability of existing studies. Therefore, using single-section LPR data, this study proposes a lane-based queue length estimation method based on a two-dimensional Gaussian mixture model. First, the LPR data were processed to obtain the departure times and time headway of vehicles. Then, the two-dimensional Gaussian distributions of queued vehicles and nonqueued vehicles were fitted, and the expectation-maximization algorithm was adopted to solve the distribution parameters. Finally, the queuing status of each vehicle was determined, and the lane-based queue length was estimated based on the last identified queued vehicle in the cycle. The empirical results showed that the mean absolute errors (MAEs) of the proposed method were just 1.3 veh/cycle under no missed detections and 2 veh/cycle under a 20% missed detection rate, outperforming existing methods. The simulation results indicate that the proposed method can achieve accurate estimates under various traffic demands. In addition, the proposed method can be extended to real-time applications and multisection LPR systems.

DoS attack detection in identification of FIR systems with binary‐valued observations

AbstractTimely and effective detection of malicious attacks is a primary problem in cyber‐physical system security. This paper studies the detection of DoS (denial of service) attacks in identification of FIR (finite impulse response) systems with binary‐valued observations. Based on the prior information of the system, an attack detection algorithm is designed, and its online form is given. The detectability is discussed for the ability of detection algorithm, and the necessary and sufficient condition for the attack strategy to be detectable is obtained. The concepts of the missed detection rate and false alarm rate are introduced, and their calculation methods and approximate formulas under large samples are provided. Furthermore, it is analyzed the influence of prior information of system parameters and data length on the performance of detection algorithm. The rationality of theoretical results is verified by numerical simulation.