Traitement Du Signal最新文献_第6页

Enhanced Campus Security Target Detection Using a Refined YOLOv7 Approach 使用改进的YOLOv7方法增强校园安全目标检测

4区计算机科学

Traitement Du Signal Pub Date : 2023-10-30 DOI: 10.18280/ts.400544

Fengyun Cao, Shuai Ma

{"title":"Enhanced Campus Security Target Detection Using a Refined YOLOv7 Approach","authors":"Fengyun Cao, Shuai Ma","doi":"10.18280/ts.400544","DOIUrl":"https://doi.org/10.18280/ts.400544","url":null,"abstract":"In many educational institutions, safety management traditionally depends upon manual video surveillance, leading to potential delays in the identification and alerting of perilous activities, notably the possession of controlled knives and smoking behaviors exhibited by students. These activities possess significant consequences for both the psychological and physical well-being of students. Recognizing this pressing need, an augmented object detection method for campus security, rooted in YOLOv7, is presented. The EIoU (Efficient Intersection over Union) loss function has been substituted to expedite model convergence and heighten detection fidelity. Additionally, the integration of the CBAM (Convolutional Block Attention Module) attention mechanism with the DCNv2 (Deformable ConvNets v2) deformable convolutional kernel not only mitigates the challenge of information inundation but also enhances feature extraction capabilities, facilitating adjustments to geometric deformations. Experimental findings indicate that this proposed method achieves a detection accuracy of 92.6% across various categories on a dataset comprising three categories, spanning a total of 4500 images, and attains an mAP of 96.4%. In comparison to the conventional YOLOv7 algorithm, enhancements in detection accuracy and mAP by 6.9% and 6.6%, respectively, have been observed, affirming the efficacy of the presented algorithm.","PeriodicalId":49430,"journal":{"name":"Traitement Du Signal","volume":"188 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136068294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Detection of Hepatic Steatosis in Ultrasound Images Using Convolutional Neural Networks: A Comparative Study 利用卷积神经网络在超声图像中有效检测肝脏脂肪变性的比较研究

4区计算机科学

Traitement Du Signal Pub Date : 2023-10-30 DOI: 10.18280/ts.400501

Fahad M. Alshagathrh, Saleh Musleh, Mahmood Alzubaidi, Jens Schneider, Mowafa S. Househ

引用次数: 0

A New Deep Learning-Based Restoration Method for Colour Images 一种新的基于深度学习的彩色图像恢复方法

4区计算机科学

Traitement Du Signal Pub Date : 2023-10-30 DOI: 10.18280/ts.400536

Songshan Zu

引用次数: 0

A Robust Algorithm for Digital Image Copyright Protection and Tampering Detection: Employing DWT, DCT, and Blowfish Techniques 数字图像版权保护和篡改检测的鲁棒算法:采用DWT、DCT和河豚技术

4区计算机科学

Traitement Du Signal Pub Date : 2023-10-30 DOI: 10.18280/ts.400520

Ahmed S. Salama, Rasha Shoitan, Mohamed S. Abdallah, Young Im Cho, Ahmad M. Nagm

引用次数: 14

Enhanced Efficiency and Security in LSB2 Steganography: Burst Embedding and Private Key Integration 提高LSB2隐写的效率和安全性:突发嵌入和私钥集成

4区计算机科学

Traitement Du Signal Pub Date : 2023-10-30 DOI: 10.18280/ts.400502

Rashad J. Rasras, Mutaz Rasmi Abu Sara, Ziad Alqadi

引用次数: 0

High-Dimension EEG Biometric Authentication Leveraging Sub-Band Cube-Code Representation 利用子带立方码表示的高维脑电图生物特征认证

4区计算机科学

Traitement Du Signal Pub Date : 2023-10-30 DOI: 10.18280/ts.400517

İdil Işıklı Esener, Onur Kılınç, Burak Urazel, Betül N. Yaman, Demet İ. Algın, Semih Ergin

{"title":"High-Dimension EEG Biometric Authentication Leveraging Sub-Band Cube-Code Representation","authors":"İdil Işıklı Esener, Onur Kılınç, Burak Urazel, Betül N. Yaman, Demet İ. Algın, Semih Ergin","doi":"10.18280/ts.400517","DOIUrl":"https://doi.org/10.18280/ts.400517","url":null,"abstract":"Advancements in EEG biometric technologies have been hindered by two persistent challenges: the management of large data sizes and the unreliability of data resulting from various measurement environments. Addressing these challenges, this study introduces a novel methodology termed 'Cube-Code' for cognitive biometric authentication. As a preliminary step, Automatic Artifact Removal (AAR) leveraging wavelet Independent Component Analysis (wICA) is applied to EEG signals. This step transforms the signals into independent sub-components, effectively eliminating the effects of muscle movements and eye blinking. Subsequently, unique 3-Dimensional (3-D) Cube-Codes are generated, each representing an individual subject in the database. Each Cube-Code is constructed by stacking the alpha, beta, and theta sub-band partitions, obtained from each channel during each task, back-to-back. This forms a third-order tensor. The stacking of these three sub-bands within a Cube-Code not only prevents a dimension increase through concatenation but also permits the direct utilization of non-stationary data, bypassing the need for fiducial component detection. Higher-Order Singular Value Decomposition (HOSVD) is then applied to perform a subspace analysis on each Cube-Code, an approach supported by previous literature concerning its effectiveness on 3-D tensors. Upon completion of the decomposition process, a flattening operation is executed to extract lower-dimensional, task-independent feature matrices for each subject. These feature matrices are then employed in five distinct deep learning architectures. The Cube-Code methodology was tested on EEG signals, composed of different tasks, from the PhysioNet EEG Motor Movement/Imagery (EEGMMI) dataset. The results demonstrate an authentication accuracy rate of approximately 98%. In conclusion, the novel Cube-Code methodology provides highly accurate results for subject recognition, delivering a new level of reliability in EEG-based biometric","PeriodicalId":49430,"journal":{"name":"Traitement Du Signal","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136104744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint Solution for Temporal-Spatial Synchronization of Multi-View Videos and Pedestrian Matching in Crowd Scenes 人群场景中多视点视频时空同步与行人匹配联合解决方案

4区计算机科学

Traitement Du Signal Pub Date : 2023-10-30 DOI: 10.18280/ts.400503

Haidong Yang, Renyong Guo

{"title":"Joint Solution for Temporal-Spatial Synchronization of Multi-View Videos and Pedestrian Matching in Crowd Scenes","authors":"Haidong Yang, Renyong Guo","doi":"10.18280/ts.400503","DOIUrl":"https://doi.org/10.18280/ts.400503","url":null,"abstract":"The study of crowd movement and behavioral patterns typically relies on spatio-temporal localization data of pedestrians. While monocular cameras serve the purpose, industrial binocular cameras based on multi-view geometry offer heightened spatial accuracy. These cameras synchronize time through circuits and are calibrated for external parameters after fixing their relative positions. Yet, the flexibility and real-time adaptability of using two different cameras or smartphones in close proximity, forming a short-baseline binocular camera, presents challenges in camera time synchronization, external parameter calibration, and pedestrian feature matching. A method is introduced herein for jointly addressing these challenges. Images are abstracted into spatial-temporal point sets based on human head coordinates and frame numbers. Through point set registration, time synchronization and pedestrian matching are achieved concurrently, followed by the calibration of the short-baseline camera's external parameters. Numerical results from synthetic and real-world scenarios indicate the proposed model's capability in addressing the aforementioned fundamental challenges. With the sole reliance on crowd image data, devoid of external hardware, software, or manual calibrations, time synchronization precision reaches the sub-millisecond level, pedestrian matching averages a 92% accuracy rate, and the camera's external parameters align with the calibration board's precision. Ultimately, this research facilitates the self-calibration, automatic time synchronization, and pedestrian matching tasks for short-baseline camera assemblies observing crowds.","PeriodicalId":49430,"journal":{"name":"Traitement Du Signal","volume":"112 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136067490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unveiling Breast Tumor Characteristics: A ResNet152V2 and Mask R-CNN Based Approach for Type and Size Recognition in Mammograms 揭示乳腺肿瘤特征:基于ResNet152V2和Mask R-CNN的乳房x光片类型和大小识别方法

4区计算机科学

Traitement Du Signal Pub Date : 2023-10-30 DOI: 10.18280/ts.400504

Chiman Haydar Salh, Abbas M. Ali

引用次数: 0

Enhanced Malware Family Classification via Image-Based Analysis Utilizing a Balance-Augmented VGG16 Model 基于图像分析的基于平衡增强VGG16模型的增强恶意软件家族分类

4区计算机科学

Traitement Du Signal Pub Date : 2023-10-30 DOI: 10.18280/ts.400534

Nagababu Pachhala, Subbaiyan Jothilakshmi, Bhanu Prakash Battula

引用次数: 0

Enhanced Identification of Internal Casting Defects in Vehicle Wheels Using YOLO Object Detection and X-Ray Inspection 基于YOLO物体检测和x射线检测的车辆车轮铸造内部缺陷识别技术

4区计算机科学

Traitement Du Signal Pub Date : 2023-10-30 DOI: 10.18280/ts.400511

Jian Da Wu, Yu Hung Huang

{"title":"Enhanced Identification of Internal Casting Defects in Vehicle Wheels Using YOLO Object Detection and X-Ray Inspection","authors":"Jian Da Wu, Yu Hung Huang","doi":"10.18280/ts.400511","DOIUrl":"https://doi.org/10.18280/ts.400511","url":null,"abstract":"In the rapidly evolving automobile industry, the safety and quality of individual vehicle components have gained paramount importance. Among these, aluminum wheels are particularly critical, given their susceptibility to internal casting defects. This study presents a novel approach to identify these defects non-destructively, employing X-ray inspection and harnessing the power of YOLO (You Only Look Once) object detection. Images of vehicle aluminum wheels were obtained via X-ray inspection, revealing the presence of internal defects. Subsequently, a periodic noise e.limination algorithm, underpinned by morphological filtering and adaptive image processing weights, was utilized to enhance the image clarity. The application of a composite cascade filter further improved the image resolution. The enhanced images were then processed using YOLO object detection, a cutting-edge technology renowned for its precision in object detection tasks. This study explores the efficacy of different YOLO model architectures in detecting and identifying internal casting defects in aluminum wheels. Our research contributes to the development of a highly accurate system for the detection of internal casting defects in vehicle wheels, offering potential improvements in vehicle safety. This methodology, pairing X-ray inspection with advanced object detection algorithms, provides a robust approach for defect identification in the production process, laying the groundwork for future advancements in vehicle component quality control.","PeriodicalId":49430,"journal":{"name":"Traitement Du Signal","volume":"321 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136067706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0