Omission Detection Research Articles

An improved YOLOv8 safety helmet wearing detection network

In the field of industrial safety, wearing helmets plays a vital role in ensuring workers’ health. Aiming at addressing the complex background in the industrial environment, caused by differences in distance, the helmet small target wearing detection methods for misdetection and omission detection problems are needed. An improved YOLOv8 safety helmet wearing detection network is proposed to enhance the capture of details, improve multiscale feature processing and improve the accuracy of small target detection by introducing Dilation-wise residual attention module, atrous spatial pyramid pooling and normalized Wasserstein distance loss function. Experiments were conducted on the SHWD dataset, and the results showed that the mAP of the improved network improved to 92.0%, which exceeded that of the traditional target detection network in terms of accuracy, recall, and other key metrics. These findings further improved the detection of helmet wearing in complex environments and greatly enhanced the accuracy of detection.

Improved Lightweight YOLOv4 Foreign Object Detection Method for Conveyor Belts Combined with CBAM

During the operation of the belt conveyor, foreign objects such as large gangue and anchor rods may be mixed into the conveyor belt, resulting in tears and fractures, which affect transportation efficiency and production safety. In this paper, we propose a lightweight target detection algorithm, GhostNet-CBAM-YOLOv4, to resolve the problem of the difficulty of detecting foreign objects at high-speed movement in an underground conveyor belt. The Kmeans++ clustering method was used to preprocess the data set to obtain the anchor box suitable for the foreign object size. The GhostNet lightweight module replaced the backbone network, reducing the model’s parameters. The CBAM attention module was introduced to enhance the ability of feature extraction facing the complex environment under the mine. The depth separable convolution was used to simplify the model structure and reduce the number of parameters and calculations. The detection accuracy of the improved method on the foreign body data set reached 99.32%, and the detection rate reached 54.7 FPS, which was 6.83% and 42.1% higher than the original YOLOv4 model, respectively. The improved method performed better than the original model on the other two datasets and could effectively avoid misdetection and omission detection. In comparison experiments with similar methods, our proposed method also demonstrated good performance, verifying its effectiveness.

Improved YOLOv5-S object detection method for optical remote sensing images based on contextual transformer

To address the problems of error and omission detection in remote sensing image detection caused by the diverse scale changes of remote sensing object scales and the abundant proportion of small-scale objects, as well as the global and dense distribution of remote sensing objects, a remote sensing image detection improvement method based on YOLOv5-S is proposed. First, according to the characteristics of remote sensing objects, the data enhancement strategy is adopted to expand the dataset samples for the characteristics of remote sensing objects to improve the generalization ability of the model. Second, the contextual transformer module is introduced to the backbone feature extraction network and the feature fusion network to ensure the local feature extraction capability while improving the global information acquisition capability of the model, making full use of the input contextual information and guiding the dynamic attention matrix learning to improve the visual representation ability. Third, based on the original model, a shallow detection scale is added, and then a multiscale complex fusion structure is adopted. Meanwhile, the K-means++ algorithm replaces the original K-means algorithm and then clusters 12 anchor box sizes. Fourth, the efficient intersection over union loss is used to improve the accuracy of the remote sensing object recognition prediction. In the experiment on the on two optical remote sensing image datasets, a comparison with several object detection algorithms based on convolutional neural network is made, the results show that the mAP@0.5 tested on the remote sensing datasets is higher than the original YOLOv5-S. Compared with other models, the detection efficiency is higher, and the problems of small-scale object detection in remote sensing image have been significantly improved.

X-Ray Small Target Security Inspection Based on TB-YOLOv5

Security inspection is extremely important for the safety of public places. In this research, we are trying to propose a novel algorithm and investigated theoretically in the X-ray dataset, which can optimize the relative low detection accuracy and the latent omission detection of smaller objects when using You Only Look Once version 5 (YOLOv5). For one side, the transform detection network is selected to be added at the bottom layer of backbone structure to avoid the loss of useful information during sequential calculation. On another side, we attempt to adjust the existing PANet structural elements of the model, including their connections and other related parameters to improve the detection performance. We integrate an efficient BiFPN with the CA mechanism, which can enhance feature extraction, and named it attention-BiFPN. Experimental consequences demonstrate that the detection accuracy of the proposed model, which we name “TB-YOLOv5,” has obvious advantages in check performance compared with the mainstream one-stage object detection models. Meanwhile, compared with YOLOv5, the data results display an improvement of up to 14.9%, and the average precision at 0.5 IOU even reached 23.4% higher in the region of small object detection. Our purpose was to explore the potential of changing a popular detection algorithm such as YOLO to address specific tasks and provide insights on how specialized adjustments can influence the detection of small objects. Our work can supply an effective method of enhancing the performance of X-ray security inspection and show promising potential for deep learning in related fields.

Automated diatom detection in forensic drowning diagnosis using a single shot multibox detector with plump receptive field

The detected result of diatoms is an important indicator in forensic drowning examination, and most of the current deep learning methods have achieved greater success in detecting diatoms with simple or no backgrounds. However, diatom images captured by the high-definition electron scanning microscopy in modern forensic science contain complex backgrounds and hamper the accurate diatom detection, resulting in the omission detection of the small and marginal diatoms in multi-diatom scenario. In this paper, we proposed a Hybrid-Dilated-Convolution-incorporated Single Shot Multibox Detector (HDC-SSD) to address this problem. By adopting the merit of the plump receptive field of HDC, the proposed algorithm not only improves the detection rate but also enhances the detection ability of the small objects and the marginal objects. The proposed method was validated by using our self-established dataset. Compared with SSD, the HDC-SSD reduces the undetected rate by approximately 48.6% and almost keeps as fast as the SSD. More importantly, compared with some current state-of-the-art methods, the HDC-SSD obtains the highest Recall value at 0.9302.

Diverted Gaze Captures Attention Easier Than Direct Gaze

Temporal orienting (TO) is the allocation of attentional resources in time based on the a priori generation of temporal expectancy of relevant stimuli as well as the a posteriori updating of this expectancy as a function of both sensory-based evidence and elapsing time. These processes rely on dissociable cognitive mechanisms and neural networks. Yet, although there is evidence that TO may be a core mechanism for cognitive functioning in childhood, the developmental spatiotemporal neural dynamics of this mechanism are little understood. In this study we employed a combined approach based on the application of distributed source reconstruction on a high spatial resolution ERP data array obtained from eighteen 8- to 12-year-old children completing a TO paradigm in which both the cue (Temporal vs. Neutral) and the SOA (Short vs. Long) were manipulated. Results show both cue (N1) and SOA (CNV, Omission Detection Potential and Anterior Anticipatory Index) ERP effects, which were associated with expectancy generation and updating, respectively. Only cue-related effects were correlated with age, as revealed by a reduction of the N1 delta effect with increasing age. Our data suggest that the neural correlates underlying TO are already established at least from 8 to 12 years of age.

Introduction

Our newest issue of the Interdisciplinary Journal of Signage and Wayfinding focuses on communication effectiveness at the intersection of consumers’ perception and their experience. This special issue features work from marketing and communication scholars who explore the visual characteristics of signage as they influence evaluations, purchase intentions, detection of omission, and compliance. Given the timing of this issue, it is not surprising that the COVID-19 pandemic provided a highly relevant context in which to study consumers’ perception of visual information. As such, two of the articles presented discuss signage within the context of the global pandemic specifically. Other articles present important research on topics related to sign complexity and consumer perceptiveness.

Tool Supported Detection of Omissions by Comparing Words between Requirements and Design Document

Tool Supported Detection of Omissions by Comparing Words between Requirements and Design Document

Adaptive threshold method for active fire identification based on GF-4 PMI data

GF-4 is China’s first geostationary orbit optical remote sensing satellite with high-resolution ground observation.Compared with other meteorological satellites,it has a single medium-wave infrared channel with the characteristics of high spatial and temporal resolution.To explore the application of GF-4 panchromatic multispectral and medium wave infrared(PMI)in forest fire detection and provide a new method for forest fire monitoring in China.Yulong autonomous county of Yunnan province,the Amur region and the outer Baikal frontier of Russia were selected as experimental areas.During the forest fires in the experimental areas in 2017 and 2018,12 scene images of GF-4 PMI were obtained,of which 8 scene images were used as the experimental group,and 4 scene images were used as the verification group.Statistical analysis was performed on the typical characteristics of the experimental group images,and the‘split window method’was used to construct an adaptive threshold detection algorithm,then the detection algorithm was used to detect the images of the verification group and compared with the results by visually interpreted.The results showed that the detection of forest fire points in Yulong autonomous county of Yunnan province,the accuracy of the algorithm in this paper was 80.0%,the omission detection rate was 20.0%,and the comprehensive evaluation index,which was based on the verification of fire detection,was 0.781.The detection of forest fire points in the outer Baikal frontier of Russia,the accuracy of the algorithm detection was 99.1%,the omission detection rate was 24.3%,and the comprehensive evaluation index was 0.858.The forest fire of 2017 in the Amur region,the accuracy of the algorithm detection was 97.7%,the omission detection rate was 22.2%,and the comprehensive evaluation index was 0.866.The forest fire of 2018 in the Amur region,the accuracy of the algorithm detection was 92.4%,the omission detection rate was 14.5%,and the comprehensive evaluation index was 0.889.The accuracy of the fire points detection in these three experimental areas was higher than 80.0%,the comprehensive evaluation index set based on the accuracy verification of the fire point detection was higher than 0.780.This algorithm could realize the fire point detection of GF-4 PMI images,and the algorithm had a higher accuracy rate of fire point detection in a large range of forest fire,but the omission detection rate of the algorithm was high and needs to be further optimized.The experimental results showed that the proposed algorithm was reliable,which could provide a method reference for forest fire monitoring in China.

In-line inspection solution for codes on complex backgrounds for the plastic container industry

Machine vision technologies have been widely used for automating the product quality control, but the defect inspection for codes on complex backgrounds is still a challenging task in the plastic container industry. In this work, an efficient and accurate inspection solution based on deep learning was proposed aiming at the detection of codes on complex backgrounds for the plastic container such as beverage packages. Firstly, image processing algorithms such as the region translation method, morphological processing, and image matching technology based on SIFT (Scale Invariant Feature Transform) features were implemented to generate synthetic defective samples, which moderated the class-imbalance problem. Data augmentation strategies were used to increase the amount of training data. Secondly, the ShuffleNet V2 framework was adapted to inspect inkjet codes on complex backgrounds. Additionally, the transfer learning was used to transfer the trained model to other inspection tasks for different kinds of packages. Finally, the proposed approach was built onto an in-line code inspection apparatus for the plastic container industry, and an accuracy of 0.9988 was achieved. The in-line testing results of false detection and omission detection rates demonstrated that the proposed solution can fully meet the production requirements. To the best of our knowledge, this report describes the first time that deep learning has been applied to the industrial defect inspection for the plastic container industry.

Battery Surface and Edge Defect Inspection Based on Sub-Regional Gaussian and Moving Average Filter

Detecting the defects of a battery on the surface and edge has always been difficult, especially for concave and convex ones, thereby seriously affecting its quality. Thus, sub-regional Gaussian and moving average filtering are innovatively proposed in this study considering the effect of the nonuniform background illumination of the battery edge and the difference between the edge background and the internal surface defects of the battery. The battery surface image is divided into two areas, namely, edge area W 1 and inner area W 2 . Gaussian and moving average filtering are carried out row-by-row and column-by-column in the inner area W 2 and the edge area W 1 , respectively. The algorithm is tested on 600 battery samples that mainly possess concave and convex defects. The proposed method has higher detection accuracy and lower omission detection rate than the traditional unpartitioned processing method, especially in detecting the accuracy of edge defects. The accuracy rates were approximately 20% higher than that obtained by the traditional processing algorithm. The proposed method has remarkable real-time performance that can process four 8192 × 10,240 pixel battery images per second, thereby meeting the industrial production line speed requirements while satisfying accuracy. The proposed method has been applied in actual production for defect inspection.

Pig Detection Algorithm Based on Sliding Windows and PCA Convolution

An accurate and rapid pig detection algorithm based on video image processing technology can be helpful to identify abnormal pigs and take timely measures to reduce the incidence of diseases. In order to solve the problems of low computational efficiency and low precision in pig detection algorithm based on sliding windows, this paper proposed a simple and efficient pig detection algorithm. A two-level support vector machine model was trained to calculate the probabilities of sliding windows by using gradient and gray distribution features of pigs. The principal component analysis convolution kernels were trained to extract foreground and background features of pig images. The support vector machine was used to classify sliding windows to obtain windows where pigs were located, and the non-maximum suppression algorithm was used to eliminate redundant windows to complete the target detection. The experiments showed that the proposed algorithm blending gradient and gray distribution features had a higher recall rate than the BING algorithm. The recall rate was up to 99.21% using 500 windows. The classification accuracy of sliding windows in this paper was 95.21%, which was higher than that of the PCANet. By calculating the omission detection rate, the misdetection rate, and the average detection time, it can be seen that in the detection methods of the proposed algorithm, BING + PCANet, faster rcnn and yolo, the performance of the proposed algorithm was optimal.

Automatic detection of consonant omission in cleft palate speech

Consonant omission is one of the most typical and common articulation disorders in cleft palate (CP) speech. The automatic evaluation of consonant omission provides an objective and aided diagnosis to speech-language pathologists and CP patients. This work proposes an automatic consonant omission method. The collection of pathologic speech data is far more difficult than that of normal speech. The speech samples applied in this work are collected from 80 CP patients, with annotation on the phonemic level by professional speech-language pathologists. The proposed method requires no pre-training or modeling, by taking advantages of priori knowledge of CP speech and Chinese phonetics. The classification between voiced initials and finals is a difficulty in Mandarin speech processing researches, this work proposes a time-domain waveform difference analysis method to address this difficulty. The proposed method achieves the accuracy of 88.9% for consonant omission detection in CP speech, and the sensitivity and specificity of the proposed method are 70.89% and 91.86% respectively.

Facilitation of temporal prediction by electrical stimulation to the primate cerebellar nuclei

The cerebellum is known to be involved in temporal information processing. However, the underlying neuronal mechanisms remain unclear. In our previous study, monkeys were trained to make a saccade in response to a single omission of periodically presented visual stimuli. To detect stimulus omission, animals had to predict the timing of each next stimulus. During this task, neurons in the cerebellar dentate nucleus exhibited a transient decrement of activity followed by a gradual increase in firing rate that peaked around the time of the next stimulus (Ohmae et al., 2013). In the present study, to address how these two components of neuronal activity contributed to omission detection, we applied electrical microstimulation to the recording site at different timing during the task. We found that electrical stimulation just before the stimulus omission shortened the latencies of both contraversive and ipsiversive saccades. Because the changes in saccade latency non-linearly depended on the timing of stimulation in each inter-stimulus interval, and electrical stimulation just before the early stimulus in the sequence failed to evoke saccades, the neuronal activity in the dentate nucleus might regulate temporal prediction rather than facilitating saccade execution. Our results support the hypothesis that the firing modulation in each inter-stimulus interval in the dentate nucleus represents neuronal code for the temporal prediction of next stimulus.

Spatiotemporally dissociable neural signatures for generating and updating expectation over time in children: A High Density-ERP study

Temporal orienting (TO) is the allocation of attentional resources in time based on the a priori generation of temporal expectancy of relevant stimuli as well as the a posteriori updating of this expectancy as a function of both sensory-based evidence and elapsing time. These processes rely on dissociable cognitive mechanisms and neural networks. Yet, although there is evidence that TO may be a core mechanism for cognitive functioning in childhood, the developmental spatiotemporal neural dynamics of this mechanism are little understood. In this study we employed a combined approach based on the application of distributed source reconstruction on a high spatial resolution ERP data array obtained from eighteen 8- to 12-year-old children completing a TO paradigm in which both the cue (Temporal vs. Neutral) and the SOA (Short vs. Long) were manipulated. Results show both cue (N1) and SOA (CNV, Omission Detection Potential and Anterior Anticipatory Index) ERP effects, which were associated with expectancy generation and updating, respectively. Only cue-related effects were correlated with age, as revealed by a reduction of the N1 delta effect with increasing age. Our data suggest that the neural correlates underlying TO are already established at least from 8 to 12 years of age.

Neural correlates of perceptual grouping effects in the processing of sound omission by musicians and nonmusicians

Perceptual grouping is the process of organizing sounds into perceptually meaningful elements. Psychological studies have found that tones presented as a regular frequency or temporal pattern are grouped according to gestalt principles, such as similarity, proximity, and good continuity. Predictive coding theory suggests that this process helps create an internal model for the prediction of sounds in a tone sequence and that an omission-related brain response reflects the violation of this prediction. However, it remains unclear which brain areas are related to this process, especially in paying attention to the stimuli. To clarify this uncertainty, the present study investigated the neural correlates of perceptual grouping effects. Using magnetoencephalography (MEG), we recorded the evoked response fields (ERFs) of amateur musicians and nonmusicians to sound omissions in tone sequences with a regular or random pattern of three different frequencies during an omission detection task. Omissions in the regular sequences were detected faster and evoked greater activity in the left Heschl's gyrus (HG), right postcentral gyrus, and bilateral superior temporal gyrus (STG) than did omissions in the irregular sequences. Additionally, an interaction between musical experience and regularity was found in the left HG/STG. Tone-evoked responses did not show this difference, indicating that the expertise effect did not reflect the superior tone processing acquired by amateur musicians due to musical training. These results suggest that perceptual grouping based on repetition of a pattern of frequencies affects the processing of omissions in tone sequences and induces more activation of the bilateral auditory cortex by violating internal models. The interaction in the left HG/STG may suggest different styles of processing for musicians and nonmusicians, although this difference was not reflected at the behavioral level.

Effects of Construal Level on Omission Detection and Multiattribute Evaluation

ABSTRACTResearch has demonstrated that consumers are commonly insensitive to missing information and that this insensitivity can lead them to form strong beliefs and evaluations on the basis of weak evidence. A growing body of research has shown that sensitivity to omissions can be heightened and that this increased sensitivity results in more appropriate evaluations. Expanding on this, the current research finds that the level of abstraction by which a situation is construed can influence the likelihood of omission detection and the resulting evaluative judgments. A series of studies reveal that people are more likely to spontaneously detect omissions in near vs. distant judgments, in concrete vs. abstract mindsets, and when they are inherently more likely to interpret actions in concrete vs. abstract terms. Further, although prior findings suggest that people may have differential sensitivity to primary and secondary missing features at different levels of construal, the current research finds no such difference. The results of this study indicate that people are more sensitive to all types of missing information when construal levels are low, and that this sensitivity leads to more moderate and appropriate judgments.

Anomaly Event Detection Method Based on Compressive Sensing and Iteration in Wireless Sensor Networks

Anomaly event detection is one of the research hotspots in wireless sensor networks. Aiming at the disadvantages of current detection solutions, a novel anomaly event detection algorithm based on compressed sensing and iteration is proposed. Firstly, a measured value can be sensed in each node, based on the compressed sensing. Then the problem of anomaly event detection is modeled as the minimization problem of weighted l1 norm, and OMP algorithm is adopted for solving the problem iteratively. And then the result of problem solving is judged according to detection functions. Finally, in the light of the judgment results, the weight value is updated for beginning a new round iteration. The loop won't stop until all the anomaly events are detected in wireless sensor networks. Simulation experimental results show the proposed algorithm has a better omission detection rate and false alarm rate in different noisy environments. In addition, the detection quality of this algorithm is higher than those of the traditional ones.

Printing Defects Detection Based on Two-Times Difference Image Method

With an aim at printing quality on-line detection, a method based on two-times difference image algorithm was proposed. Firstly, a standard template image and a gray threshold value image were calculated by using statistical methods. Secondly, an abnormal spots image was obtained through two-times difference image of the detection image, the standard template image and gray threshold value image. Lastly, the defects can be detected by analysis of connected region of the abnormal spots image. The results demonstrated that the problems of false detection and omission detection due to edge of image can be solved effectively, and the detection accuracy can be improved through this method.

Automatic detection of omissions in medication lists

Evidence suggests that the medication lists of patients are often incomplete and could negatively affect patient outcomes. In this article, the authors propose the application of collaborative filtering methods to the medication reconciliation task. Given a current medication list for a patient, the authors employ collaborative filtering approaches to predict drugs the patient could be taking but are missing from their observed list. The collaborative filtering approach presented in this paper emerges from the insight that an omission in a medication list is analogous to an item a consumer might purchase from a product list. Online retailers use collaborative filtering to recommend relevant products using retrospective purchase data. In this article, the authors argue that patient information in electronic medical records, combined with artificial intelligence methods, can enhance medication reconciliation. The authors formulate the detection of omissions in medication lists as a collaborative filtering problem. Detection of omissions is accomplished using several machine-learning approaches. The effectiveness of these approaches is evaluated using medication data from three long-term care centers. The authors also propose several decision-theoretic extensions to the methodology for incorporating medical knowledge into recommendations. Results show that collaborative filtering identifies the missing drug in the top-10 list about 40-50% of the time and the therapeutic class of the missing drug 50%-65% of the time at the three clinics in this study. Results suggest that collaborative filtering can be a valuable tool for reconciling medication lists, complementing currently recommended process-driven approaches. However, a one-size-fits-all approach is not optimal, and consideration should be given to context (eg, types of patients and drug regimens) and consequence (eg, the impact of omission on outcomes).