Accuracy Of Boundary Detection Research Articles

A lightweight waxberry fruit detection model based on YOLOv5

A lightweight waxberry fruit detection model based on YOLOv5

YOLO v7-CS: A YOLO v7-Based Model for Lightweight Bayberry Target Detection Count

In order to estimate bayberry yield, a lightweight bayberry target detection count model, YOLOv7-CS, based on YOLOv7, was proposed to address the issues of slow detection and recognition speed, as well as low recognition rate, of high-density bayberry targets under complex backgrounds. In this study, 8990 bayberry images were used for experiments. The training set, validation set, and test set were randomly recreated in a ratio of 8:1:1. The new network was developed with SPD-Conv detection head modules to extract features at various scales, to better capture small and indistinct bayberry targets. To improve accuracy and achieve a lightweight design, a CNxP module that replaces the backbone’s ELAN structure is proposed. We propose a global attention mechanism (GAM) in the intermediate layers of the network, to enhance cross-dimensional interactions, and a new pyramid pooling module called SPPFCSPC, to extend the field of perception and improve boundary detection accuracy. Finally, we combine the Wise-IoU function to enhance the network’s ability to identify overlapping and occluded objects. Compared with the SSD, Faster-RCNN, DSSD, and YOLOv7X target detection algorithms, YOLOv7-CS increases mAP 0.5 by 35.52%, 56.74%, 12.36%, and 7.05%. Compared with basic YOLOv7, mAP 0.5 increased from 5.43% to 90.21%, while mAP 0.95 increased from 13.2% to 54.67%. This parameter is reduced by 17.3 m. Ablation experiments further show that the designed module improves the accuracy of bayberry detection, reduces parameter counts, and makes bayberry image detection more accurate and effective.

Optimized Deep Learning for Congestion-Aware Continuous Target Tracking and Boundary Detection in IoT-Assisted WSN

Target tracking (TT) is an important application of Internet of Things (IoT)-assisted wireless sensor networks (WSNs) in which sensor nodes monitor and report the positions of continuously moving objects. However, continuous tracking of multiple objects causes network congestion. To solve this challenge and provide congestion-aware continuous TT and boundary detection, we propose a novel energy-aware radial clustering based piecewise regressive multiobjective golden eagle optimized deep convolutive learning (EARC-ODCL) algorithm. In particular, EARC-ODCL incorporates a multiobjective stochastic sampled golden eagle optimization to select the optimal cluster heads (CHs) for sending the target information. Also, a fully connected deep convolutive neural learning is used to identify the boundary of sensor nodes. Furthermore, a piecewise linear regression approach is used for network congestion prediction and minimize data loss. Thereby, the proposed EARC-ODCL is able to perform continuous TT and boundary detection with higher accuracy in IoT-assisted WSN. Extensive simulations are conducted to assess the performance of EARC-ODCL in terms of various metrics, such as TT accuracy and time, boundary detection accuracy (BDA), and data loss.

A Visual Tracking Algorithm Combining Parallel Network and Dual Attention-Aware Mechanism

In order to solve the problems of semantic loss and inaccurate boundary detection in the process of object tracking, a visual tracking algorithm combining parallel structure and dual attention-attention mechanism is proposed in this paper. As backbone network, parallel structure is composed of Convolutional neural network and Attention Cooperative(CAC) processing module, which is used for feature extraction. Because this structure can capture the local and global information of the target at the same time, it can solve the problem of semantic information loss. Dual Attention-aware Network(DAN) is used for feature enhancement, which is composed of target-aware attention and boundary-aware attention, and the accuracy of boundary detection is enhanced by feature pyramid structure. Template online updating strategy is used to improve template quality, and an effective score prediction module-Template Elimination Mechanism(TEM) is designed in the CAC processing module to select high quality templates. This kind of object tracking algorithm which combines local and global information is called TrackCAC. The evaluation results on different datasets show that the algorithm can maintain high tracking precision and success in different scenarios. It shows good robustness and accuracy in the performance evaluation results on VOT datasets.

Efficient contour-based annotation by iterative deep learning for organ segmentation from volumetric medical images

PurposeTraining deep neural networks usually require a large number of human-annotated data. For organ segmentation from volumetric medical images, human annotation is tedious and inefficient. To save human labour and to accelerate the training process, the strategy of annotation by iterative deep learning recently becomes popular in the research community. However, due to the lack of domain knowledge or efficient human-interaction tools, the current AID methods still suffer from long training time and high annotation burden.MethodsWe develop a contour-based annotation by iterative deep learning (AID) algorithm which uses boundary representation instead of voxel labels to incorporate high-level organ shape knowledge. We propose a contour segmentation network with a multi-scale feature extraction backbone to improve the boundary detection accuracy. We also developed a contour-based human-intervention method to facilitate easy adjustments of organ boundaries. By combining the contour-based segmentation network and the contour-adjustment intervention method, our algorithm achieves fast few-shot learning and efficient human proofreading.ResultsFor validation, two human operators independently annotated four abdominal organs in computed tomography (CT) images using our method and two compared methods, i.e. a traditional contour-interpolation method and a state-of-the-art (SOTA) convolutional network (CNN) method based on voxel label representation. Compared to these methods, our approach considerably saved annotation time and reduced inter-rater variabilities. Our contour detection network also outperforms the SOTA nnU-Net in producing anatomically plausible organ shape with only a small training set.ConclusionTaking advantage of the boundary shape prior and the contour representation, our method is more efficient, more accurate and less prone to inter-operator variability than the SOTA AID methods for organ segmentation from volumetric medical images. The good shape learning ability and flexible boundary adjustment function make it suitable for fast annotation of organ structures with regular shape.

Edge Detection of COVID-19 CT Image Based on GF_SSR, Improved Multiscale Morphology, and Adaptive Threshold

Edge detection is an effective method for image segmentation and feature extraction. Therefore, extracting weak edges with the inhomogeneous gray of Corona Virus Disease 2019 (COVID-19) CT images is extremely important. Multiscale morphology has been widely used in the edge detection of medical images due to its excellent boundary detection accuracy. In this paper, we propose a weak edge detection method based on Gaussian filtering and singlescale Retinex (GF_SSR), and improved multiscale morphology and adaptive threshold binarization (IMSM_ATB). As all the CT images have noise, we propose to remove image noise by Gaussian filtering. The edge of CT images is enhanced using the SSR algorithm. In addition, based on the extracted edge of CT images using improved Multiscale morphology, a particle swarm optimization (PSO) algorithm is introduced to binarize the image by automatically getting the optimal threshold. To evaluate our method, we use images from three datasets, namely COVID-19, Kaggle-COVID-19, and COVID-Chestxray, respectively. The average values of results are worthy of reference, with the Shannon information entropy of 1.8539, the Precision of 0.9992, the Recall of 0.8224, the F-Score of 1.9158, running time of 11.3000. Finally, three types of lesion images in the COVID-19 dataset are selected to evaluate the visual effects of the proposed algorithm. Compared with the other four algorithms, the proposed algorithm effectively detects the weak edge of the lesion and provides help for image segmentation and feature extraction.

Acoustic Identification of the Voicing Boundary during Intervocalic Offsets and Onsets based on Vocal Fold Vibratory Measures.

Methods for automating relative fundamental frequency (RFF)—an acoustic estimate of laryngeal tension—rely on manual identification of voiced/unvoiced boundaries from acoustic signals. This study determined the effect of incorporating features derived from vocal fold vibratory transitions for acoustic boundary detection. Simultaneous microphone and flexible nasendoscope recordings were collected from adults with typical voices (N=69) and with voices characterized by excessive laryngeal tension (N=53) producing voiced–unvoiced–voiced utterances. Acoustic features that coincided with vocal fold vibratory transitions were identified and incorporated into an automated RFF algorithm (“aRFF-APH”). Voiced/unvoiced boundary detection accuracy was compared between the aRFF-APH algorithm, a recently published version of the automated RFF algorithm (“aRFF-AP”), and gold-standard, manual RFF estimation. Chi-square tests were performed to characterize differences in boundary cycle identification accuracy among the three RFF estimation methods. Voiced/unvoiced boundary detection accuracy significantly differed by RFF estimation method for voicing offsets and onsets. Of 7721 productions, 76.0% of boundaries were accurately identified via the aRFF-APH algorithm, compared to 70.3% with the aRFF-AP algorithm and 20.4% with manual estimation. Incorporating acoustic features that corresponded with voiced/unvoiced boundaries led to improvements in boundary detection accuracy that surpassed the gold-standard method for calculating RFF.

A Research on Landslides Automatic Extraction Model Based on the Improved Mask R-CNN

Landslides are the most common and destructive secondary geological hazards caused by earthquakes. It is difficult to extract landslides automatically based on remote sensing data, which is import for the scenario of disaster emergency rescue. The literature review showed that the current landslides extraction methods mostly depend on expert interpretation which was low automation and thus was unable to provide sufficient information for earthquake rescue in time. To solve the above problem, an end-to-end improved Mask R-CNN model was proposed. The main innovations of this paper were (1) replacing the feature extraction layer with an effective ResNeXt module to extract the landslides. (2) Increasing the bottom-up channel in the feature pyramid network to make full use of low-level positioning and high-level semantic information. (3) Adding edge losses to the loss function to improve the accuracy of the landslide boundary detection accuracy. At the end of this paper, Jiuzhaigou County, Sichuan Province, was used as the study area to evaluate the new model. Results showed that the new method had a precision of 95.8%, a recall of 93.1%, and an overall accuracy (OA) of 94.7%. Compared with the traditional Mask R-CNN model, they have been significantly improved by 13.9%, 13.4%, and 9.9%, respectively. It was proved that the new method was effective in the landslides automatic extraction.

Improved Fuzzy-Based SVM Classification System Using Feature Extraction for Video Indexing and Retrieval

Various researches have been performed with video abstraction with the constant development of multimedia technology. However, there are some deficiencies that have been encountered in the pre-processing of video frames before attaining classified video archives. To overcome the drawbacks in pre-processing, feature extraction and classification approaches are considered. Here, video indexing has been anticipated with several features’ extraction with dominant frame generation for the input video frame. Fuzzy-based SVM classifier is utilized to categorize frame set into dominant structures. Multi-dimensional Histogram of Oriented Gradients (HOG) and colour feature extraction are used to extract texture features from the video frame. Using the frame sequence, the vector space of structures is captured; dominant frameworks are utilized in video indexing. Shot transitions’ classification is done with a fuzzy system. Experimental outcomes demonstrate that shot boundary detection accuracy increases with an increase in iterations. The simulation was carried out in MATLAB environment. This technique attains an accuracy of about 95.4%, the precision of 100%, and the F1 score of 100% and a recall of 100%. The misclassification rate is 4.6%. The proposed method shows better trade-off than the existing techniques.

Building Footprint Extraction from Multispectral, Spaceborne Earth Observation Datasets Using a Structurally Optimized U-Net Convolutional Neural Network

Building footprint detection and outlining from satellite imagery represents a very useful tool in many types of applications, ranging from population mapping to the monitoring of illegal development, from urban expansion monitoring to organizing prompter and more effective rescuer response in the case of catastrophic events. The problem of detecting building footprints in optical, multispectral satellite data is not easy to solve in a general way due to the extreme variability of material, shape, spatial, and spectral patterns that may come with disparate environmental conditions and construction practices rooted in different places across the globe. This difficult problem has been tackled in many different ways since multispectral satellite data at a sufficient spatial resolution started making its appearance on the public scene at the turn of the century. Whereas a typical approach, until recently, hinged on various combinations of spectral–spatial analysis and image processing techniques, in more recent times, the role of machine learning has undergone a progressive expansion. This is also testified by the appearance of online challenges like SpaceNet, which invite scholars to submit their own artificial intelligence (AI)-based, tailored solutions for building footprint detection in satellite data, and automatically compare and rank by accuracy the proposed maps. In this framework, after reviewing the state-of-the-art on this subject, we came to the conclusion that some improvement could be contributed to the so-called U-Net architecture, which has shown to be promising in this respect. In this work, we focused on the architecture of the U-Net to develop a suitable version for this task, capable of competing with the accuracy levels of past SpaceNet competition winners using only one model and one type of data. This achievement could pave the way for achieving better performances than the current state-of-the-art. All these results, indeed, have yet to be augmented through the integration of techniques that in the past have demonstrated a capability of improving the detection accuracy of U-net-based footprint detectors. The most notable cases are represented by an ensemble of different U-Net architectures, the integration of distance transform to improve boundary detection accuracy, and the incorporation of ancillary geospatial data on buildings. Our future work will incorporate those enhancements.

Prosodic word boundary detection from Bengali continuous speech

Detection of word boundaries in continuous speech is a tedious process due to the absence of a definite pause or silence in the word boundary position. Thus, continuous speech recognition is a very challenging task. However, the prosodic word boundaries, unlike the written word boundaries, can be predicted using the prosodic parameters of continuous speech. This paper proposes a method for detecting such prosodic word boundaries from Bengali continuous speech. Bengali is a bound-stress language, where stress is observed on the first syllable of a prosodic word. Empirical Mode Decomposition is applied to the logarithm of fundamental frequency (F0) contour of continuous speech to detect prosodic word boundaries. 200 Bengali readout sentences, read by ten speakers, are analyzed for the present work. An overall prosodic boundary detection accuracy of 88.05% is achieved, whereas precision and recall values are 90.73% and 88.31%, respectively, with f-score as 89.5. A prosodic word dictionary comprising 5031 prosodic words has been developed by analyzing 1526 Bengali sentences with the proposed prosodic word boundary detection method.

A Precise and Stable Segmentation Algorithm of SAR Images Based on Random Weighting Method and Modified Level Set

Level set methods have been widely used for image segmentation due to their good boundary detection accuracy. In the context of synthetic aperture radar (SAR) image segmentation, the presence of speckles and the distribution estimation of SAR image remain important issues that may hinder the accuracy of any segmentation method based on level set methods. In this paper, we propose a more accurate and a stable segmentation method based on the random weighting method and modified threshold-based level set energy functional. The proposed method uses a level set evolution that is based on the minimization of an objective energy functional, whose propagation function is based on the ${\mathcal{ G}}^{0}$ statistical model, whose parameters are estimated by random weighting estimators, and the estimator is not affected by the hypothesized model and sampling number. In addition, a new regularization item and length term, which maintain the regularity of the level set function and contour respectively, were employed. The experimental results demonstrate that the proposed methodology has a good and stable capability of segmentation, both in homogeneous, heterogeneous, and extremely heterogeneous regions in SAR image.

Analysis of Lung Tumour Detection and Segmentation Using Level Set Method of Active Contour Model

Lung cancer is the most common leading cancer in both men and women all over the world. Accurate image segmentation is an essential image analysis tool that is responsible for partitioning an image into several sub-regions. Active contour model have been widely used for effective image segmentation methods as this model produce sub-regions with continuous boundaries. It is used in the applications such as image analysis, deep learning, computer vision and machine learning. Advanced level set method helps to implement active contours for image segmentation with good boundary detection accuracy. This paper proposes a model based on active contour using level set methods for segmentation of such lung CT images and focusing 3D lesion refinement. The features were determined by applying a multi-scale Gaussian filter. This proposed method is able to detect lung tumors in CT images with intensity, homogeneity and noise. The proposed method uses LIDC-IDRI dataset images to segment accurate 3D lesion of lung tumor CT images.

Primary User Boundary Detection in Cognitive Radio Networks: Estimated Secondary User Locations and Impact of Malicious Secondary Users

Cognitive radio is an effective technology to improve spectrum utilization through spectrum sharing among primary users (PUs) and secondary users (SUs). Developing accurate and efficient methods to determine the PU coverage is crucial in order to avoid potential interference from SU to PU. In this paper, we propose to use the estimated location of each SU, which is obtained using the self-organizing maps algorithm, to determine the boundary of PU coverage. Furthermore, the PU boundary detection issue considering the presence of malicious SUs is also investigated. Support vector machine (SVM) algorithm, considering both linear and kernel cases, is implemented as the PU boundary detection method. Numerical results show that the boundary detection accuracy based on the estimated network layout approaches the results obtained from an accurate network layout (i.e., each SU with precise location information) and the kernel-based SVM classification results outperform the linear SVM method. In addition, PU boundary detection performance in the presence of malicious SUs is evaluated, in which three different $K$ nearest neighbor based malicious SU detection algorithms are utilized.

An Online Distributed Boundary Detection and Classification Algorithm for Mobile Sensor Networks

We present a novel online distributed boundary detection and classification algorithm in order to improve accuracy of boundary detection and classification for mobile sensor networks. This algorithm is developed by incorporating a boundary detection algorithm and our newly proposed boundary error correction algorithm. It is a fully distributed algorithm based on the geometric approach allowing to remove boundary errors without recursive process and global synchronization. Moreover, the algorithm allows mobile nodes to identify their states corresponding to their positions in network topologies, leading to self-classification of interior and exterior boundaries of network topologies. We have demonstrated effectiveness ofthis algorithm in both simulation and real-world experiments and proved that the accuracy of the ratio of correctly identified nodes over the total number of nodes is 100%.

Weighted Level Set Evolution Based on Local Edge Features for Medical Image Segmentation

Level set methods have been widely used to implement active contours for image segmentation applications due to their good boundary detection accuracy. In the context of medical image segmentation, weak edges and inhomogeneities remain important issues that may hinder the accuracy of any segmentation method based on active contours implemented using level set methods. This paper proposes a method based on active contours implemented using level set methods for segmentation of such medical images. The proposed method uses a level set evolution that is based on the minimization of an objective energy functional whose energy terms are weighted according to their relative importance in detecting boundaries. This relative importance is computed based on local edge features collected from the adjacent region located inside and outside of the evolving contour. The local edge features employed are the edge intensity and the degree of alignment between the image's gradient vector flow field and the evolving contour's normal. We evaluate the proposed method for segmentation of various regions in real MRI and CT slices, X-ray images, and ultra sound images. Evaluation results confirm the advantage of weighting energy forces using local edge features to reduce leakage. These results also show that the proposed method leads to more accurate boundary detection results than state-of-the-art edge-based level set segmentation methods, particularly around weak edges.

Robust boundary detection of left ventricles on ultrasound images using ASM-level set method.

Level set method has been widely used in medical image analysis, but it has difficulties when being used in the segmentation of left ventricular (LV) boundaries on echocardiography images because the boundaries are not very distinguish, and the signal-to-noise ratio of echocardiography images is not very high. In this paper, we introduce the Active Shape Model (ASM) into the traditional level set method to enforce shape constraints. It improves the accuracy of boundary detection and makes the evolution more efficient. The experiments conducted on the real cardiac ultrasound image sequences show a positive and promising result.

HYBRID BOUNDARY DETECTION METHOD FOR IMAGE WITH APPLICATION TO CORONARY PLAQUE

This paper proposes a hybrid boundary detection method for image based on a new modified level set method and a fuzzy model. It is applied to a boundary detection problem of coronary plaque. Level set method has been applied widely in image processing. It however does not work well for an intravascular ultrasound (IVUS) image because an image gradient, commonly used for calculating a speed function in the level set method, cannot detect an image boundary well. The level set method and the weighted image separability proposed by the authors in the past were applied for a coronary plaque boundary detection problem. The level set method could not however detect the plaque boundary in several regions. One problem was that the candidates of the plaque boundary detected by the weighted separability were unclear in several regions. The other problem was that the IVUS image often becomes shadowed and it contains no texture information there, which is caused by the presence of the guide wire. To overcome this problem, we propose a new modified level set, and we further propose a hybrid boundary detection method based on the new modified level set and the Takagi Sugeno (T-S) fuzzy model for detecting a coronary plaque boundary. The boundary detection accuracy of the proposed method was significantly better than those of the previous methods we proposed in the past.

A perceptual scheme for fully automatic video shot boundary detection

In this paper, we propose a novel and robust modus operandi for fast and accurate shot boundary detection where the whole design philosophy is based on human perceptual rules and the well-known “Information Seeking Mantra”. By adopting a top–down approach, redundant video processing is avoided and furthermore elegant shot boundary detection accuracy is obtained under significantly low computational costs. Objects within shots are detected via local image features and used for revealing visual discontinuities among shots. The proposed method can be used for detecting all types of gradual transitions as well as abrupt changes. Another important feature is that the proposed method is fully generic, which can be applied to any video content without requiring any training or tuning in advance. Furthermore, it allows a user interaction to direct the SBD process to the user's “Region of Interest” or to stop it once satisfactory results are obtained. Experimental results demonstrate that the proposed algorithm achieves superior computational times compared to the state-of-art methods without sacrificing performance.

Detection of Boundaries of Carotid Arterial Wall by Analyzing Ultrasonic RF Signals

In line with the fact that the intima–media thickness (IMT) of the carotid arterial wall is the most frequently used indicator to diagnose atherosclerosis by ultrasound, it is essential to accurately estimate the thickness of the intima–media complex (IMC) boundaries, i.e., the lumen–intima boundary (LIB) and media–adventitia boundary (MAB). In this study, an improved adaptive model of an ultrasonic echo was developed for the model to realize better fitting to the reference RF echo, which is measured from a glass plate, using a Gaussian window for the envelope function of the adaptive model. Using the mean squared error (MSE) method, the envelope of the improved adaptive model (multiply the sinusoidal wave with the Gaussian window) was fitted with the envelope of an RF echo measured in vivo to estimate the boundaries of the carotid arterial wall. Firstly, a computer simulation of the carotid arterial wall was conducted to evaluate the accuracy of boundary detection using the envelope of the improved adaptive model. In the simulation, IMT was set at 0.50 mm in a 7.2-mm-long short segment in the longitudinal direction. The IMT estimated by the proposed method was 0.54 mm. The 8% error between the true and detected IMTs showed the high accuracy of the envelope of the improved adaptive model in boundary detection. In the in vivo measurement, for the 4.8-mm-long short segment in the longitudinal direction, the average IMT automatically estimated by the proposed method was 0.57 mm. The result was compared with those obtained by the previous method and manually. The IMT estimated by the previous method, which uses an RF adaptive model, was 0.59 mm and the manually determined IMT was 0.56 mm. The smaller difference between the results obtained by the proposed method and manually verified that boundary detection by the proposed method was better than that by the previous method.