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New fire disaster detection algorithms based on YOLOv4

New fire disaster detection algorithms based on YOLOv4

引用

ieee international conference on Cloud computing and Intelligence Systems (CCIS)

作者： HuiJun Su Zhihong Chen Haiwei Zhang Shuanglong Xu Institute of Integrated Circuit Science and Engineering Tianjin University of Technology Tianjin China

ISBN: (纸本)9781665456579

the fast growth of the global economy in recent years has resulted in a variety of natural catastrophes, particularly fire, which not only poses a significant threat to our property but also causes more serious issues like environmental contamination, harming people’s health. So, it is critical to identify fires to minimize losses and damages. By comparing five algorithms, two better techniques of fire detection based on the YOLOV4 algorithm are suggested in this paper. the upgraded model SK-YOLOV4 obtains an average accuracy of 80.82% on fire photos and a detection speed of 27.59 FPS. M3-YOLOV4 shows that the accuracy rises by 4.18% when compared to YOLOv4. the recommended design reduces the number of parameters by 81.68% while increasing inference speed by 9.48 FPS. the methods outlined can be used as a reference to accurately and rapidly identify the beginning of fire.

关键词： Resistance Convolution Semantic segmentation object detection Feature extraction Inference algorithms Kernel

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Novel-domain object Segmentation via Reliability-aware Teacher Ensemble

Novel-domain Object Segmentation via Reliability-aware Teach...

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ieee international conference on High Performance computing and Communications (HPCC)

作者： Jing Miao Wei Luo Nayyar Zaidi Jinlong Wang Qingdao University of Technology Qingdao China Deakin University Geelong Australia

object segmentation in a novel domain is challenging due to the scarcity of high-quality label data. A common practice is to leverage pseudolabels generated from a teacher model pretrained on benchmark datasets. However, such a teacher model may produce unreliable pseudolabels due to semantic shift from a standard domain to a novel domain. Blindly trusting pseudolabels may actually harm the target model's performance. To address this challenge, we propose a method to optimally adapt pseudolabels to work on a novel domain. At the center of our approach is a reliability score for pseudolabels that allows for an optimal mix of true labels and pseudolabels in training. Mirroring the standard practise of creating a benchmark dataset (such as ImageNet or GoEmotions), we use an ensemble of teacher models, instead of a single teacher model. Our reliability score captures both self-confidence of each teacher model and inter-model consistency among teacher models. this reliability score provides a disciplined way to balance the additional information in pseudolabels and the risk of overfitting incorrect labels. To test our approach for novel-domain object segmentation, we created an oil-leak surveillance dataset, which we have made publicly available. the experimental results show that our method achieves higher segmentation IoU over some strong baselines.

关键词： Training Adaptation models Smart cities Surveillance Semantics object segmentation Benchmark testing

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YOLOv3-XH: More Accurate object Detection Using Feature Enhancement and Fusion

YOLOv3-XH: More Accurate Object Detection Using Feature Enha...

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international conference on Computer and Communications (ICCC)

作者： Xiaohui Wang Zhenyu Yin Chao Fan Jiong Li Zhiying Bi Hui Li Chinese Academy of Sciences Liaoning Key Laboratory of Domestic Industrial Control Platform Technology on Basic Hardware and Software University of Chinese Academy of Sciences Shenyang Institute of Computing Technology Shenyang China Liaoning Key Laboratory of Domestic Industrial Control Platform Technology on Basic Hardware and Software Shenyang Institute of Computing Technology Shenyang China

object detection is an important basis for understanding the high-level semantic information of images. To address the problems of small object accuracy and inaccurate bounding box localization in the YOLOv3, we propose a new object detection method, YOLOv3-XH. In YOLOv3-XH, we make three improvements to YOLOv3. Firstly, we propose the Enhanced Feature Pyramid Network (EFPN), which fuses Path Aggregation Network (PAN) and Feature Pyramid Network (FPN) and helps extract richer feature information and increase the accuracy of small object detection. Secondly, the Multi-Branch Receptive Field (MBRF) is proposed, which adds a branch to RFB for feature extraction and helps to increase the receptive field and enhance feature extraction. Finally, a new loss function is proposed, using GIo U loss as the bounding box loss function, so that the network is optimised in the direction of a higher overlap between the predicted and real boxes. Our approach achieved an average mean accuracy mAP of 83.02% (IoU=O.5) on the PASCAL VOC test set, reaching an improvement of 1.83%.

关键词： Location awareness Fuses Computational modeling Semantics object detection Feature extraction Data mining

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A Feature-Based Approach for Organizing Spatial Data of Digital Twin Railway

A Feature-Based Approach for Organizing Spatial Data of Digi...

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ieee international conference on Intelligent Transportation Engineering (ICITE)

作者： Zhibo Cheng Yanhua Wu Shubao Song Yongxin Guo Zhong Luan Zheqian Liu Institute of Computing Technologies China Academy of Railway Sciences Corporation Limited Beijing China Faculty of Geosciences and Environmental Engineering Southwest Jiaotong University Chengdu China

ISBN: (数字)9798350314212

ISBN: (纸本)9798350314229

In response to the challenge of maintaining consistency in storing and processing geometry and semantics in the spatial data model of digital twin railway, a feature-based railway spatial data model is conducted by analyzing the establishing requirement of the railway spatial data model and combining the linear distribution characteristics of the railway. the focus was on providing a detailed discussion of the feature sets and their interrelationships within the conceptual model. Additionally, package diagrams and class diagrams using UML language to express features, which were employed to construct the logical model, and express the description of railway feature objects. through comparative analysis, it was demonstrated that this model enables the consistent description of the geometry, location, and semantics of feature objects. It fully expresses the complex relationships present in the geographic world, more aligning with human cognitive laws regarding geographic entities.

关键词： Geometry Analytical models object oriented modeling Unified modeling language Semantics Transportation Transforms Data models Spatial databases Digital twins

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Augmented Reality Application for Book Detection with Google Colaboratory and SnapML Lens Studio

Augmented Reality Application for Book Detection with Google...

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international conference on computing, Engineering, and Design (ICCED)

作者： Galuh Saputri Joko Suwamo Achmad Lutfi Fuadi Agus Heri Yunial Program Study Informatics EngineeringFaculty of Engineering Universitas Pamulang Tangerang Selatan Banten Indonesia

ISBN: (纸本)9781665453905

Artificial intelligence is one of the technological breakthroughs in science, especially in the field of computers. object detection and recognition presents a new challenge on how to make machines detect objects automatically. To detect an object, the previous system will take the classifier of an object and evaluate it at various locations and various scales in the image. Smartphones currently play an important role in everyday life, but the existence of smartphones can be one of the causes of the user's lack of interest in reading books. With this Augmented Reality-based object detector, it is hoped that users can experiment on smartphones with the Snapchat application installed to detect objects in books that are around them and the number of objects will be counted automatically, it is also expected to attract users' desire to read books. Image recognition identification is taken through the phone's camera which is recognized as a lens. Lens AR in the Lens Studio application is supported by the SnapML machine learning algorithm, this machine learning function is to display book information and the number of books detected. this research was conducted using Cloud computing such as Google Collaboratory which uses the python programming language, the book object dataset is selected from the COCO class, the model is exported to Open Neural Network Exchange (.ONNX) and then imported into the Lens Studio application. Publish lenses in the Snapchat app. Finally, a model test was carried out to detect objects and the number of objects in books that had been made using a Smartphone. Open the Snapchat application, scan the Snapcode generated by Lens Studio, AR lenses can be used in real time. AR Book Detection has been tested by other users on Snapchat with 18,845 Lens Views, 18,737 Lens Plays and 16 Lens Shares.

关键词： Image recognition Machine learning algorithms Computational modeling Multimedia Web sites Neural networks object detection Real-time systems

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Aprus: An Airborne Altitude-Adaptive Purpose-Related UAV System for object Detection

Aprus: An Airborne Altitude-Adaptive Purpose-Related UAV Sys...

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ieee international conference on High Performance computing and Communications (HPCC)

作者： Weiqiang Wang Haiyang Chen Xingzhou Zhang Wei Zhou Weisong Shi Engineering Research Center of Cyberspace Yunnan University Kunming China School of Software Yunnan University Kunming China Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Department of Computer and Information Sciences University of Delaware Newark DE USA

Unmanned aerial vehicle (UAV) system plays an important role in new edge scenarios such as disaster relief, situational awareness, and so on. In order to improve efficiency, computing tasks such as object detection are carried out on the drone-mounted computing unit. However, there is a trade-off between flight altitude and mission accuracy. Higher altitudes result in smaller images, which influence the accuracy. In comparison, shorter altitudes reduce the difficulty of detection while decreasing the recognition range, which affects user experience. therefore, this paper proposes an airborne altitude adaptive UAV System, Aprus, which selects the most suitable object detection models based on a self-adaptation strategy at different altitudes. the selection strategy is based on a purpose-related evaluation indicator, PEI. It comprehensively considers the model's accuracy, recall, and inference speed at the current altitude according to the user's purpose. Moreover, Aprus sends the original image to a divider instead of scaling the image before pushing it to the model, thus ensuring that the original picture information will not be lost. To evaluate the system, we build a high-resolution UAV dataset with altitude, UDWA, which contains 46037 images. From the experiments, Aprus obtained 58.52% mAP, 94.17%mAP 50 , 66.17%mAR, 1.61FPS results on DJI Manifold 2G when the purpose is set to Balance mode, and the system can be adjusted by multiple preset purposes or according to the user customs.

关键词： Meters Manifolds Adaptation models Image resolution Atmospheric modeling object detection Autonomous aerial vehicles

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Evaluating Serverless Architecture for Big Data enterprise Applications 21

Evaluating Serverless Architecture for Big Data Enterprise A...

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2021 ieee/ACM 8th international conference on Big Data computing, Applications and Technologies (BDCAT '21)

作者： Aimer Bhat Heeki Park Madhumonti Roy Computer Science University of Illinois Urbana Champaign USA

ISBN: (纸本)9781450391641

Migration of enterprise applications to the cloud has been driven by a myriad of benefits ranging from availability of infinite computing resources to the elimination of upfront CapEx cost. However, many users still face the burden of complex framework knowledge requirement to efficiently deploy applications in the cloud. In this paper, we investigate serverless computing for performing large scale data processing with cloud-native primitives. Serverless computing environment abstracts all infrastructure handling, simplifying developers’ work who aspire to deploy applications on the cloud. With dynamic input load on the system, serverless architecture has promise to provide better resource utilization and lower costs.

关键词： Serverless computing cloud computing Function as a Service Big data processing MapReduce enterprise applications AWS Lambda load variations

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Optimizing Comparisons in the Construction of Insertion Tableau for Permutations

Optimizing Comparisons in the Construction of Insertion Tabl...

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conference on Information and Communication Technology (CICT)

作者： Shweta Godse Varunkumar Jayapaul School of Computing and Electrical Engineering IIT Mandi Kamand Himachal Pradesh

ISBN: (数字)9798331531539

ISBN: (纸本)9798331531546

Young Tableaux are combinatorial object which have found utility in areas such as combinatorics, cryptography, representation theory etc. Every application has an inherent need for constructing a Young Tableaux efficiently. Young Tableaux consists of two parts namely the Insertion Tableau(P) and the Recording Tableau(Q). the procedure of transforming the input permutation $(\sigma)$ into tableau is known as the RSK algorithm. Each element from permutation is inserted into $P$ , consequently displacing/bumping other elements from the tableau, whereas $Q$ records the order in which these elements were inserted into $P$ . As part of our work, we have devised a modified method for the construction of insertion tableau from the permutation $(\sigma)$ , the number of comparisons mainly take place in searching for a position where the next element can be inserted in $P$ . In order to solve this problem, we consider $P$ along with the pointer matrix which stores the pointers to the elements in $P$ and tracks previous points of insertion to improve subsequent insertions. We use a combination of linear search and binary search on $P$ to find the correct location for inserting an element $(\alpha)$ from the permutation $(\sigma)$ . Our randomized trials show that the devised algorithm beats the standard RSK algorithm which uses binary search and the Fast RSK algorithm (current theoretical best) devised by Tiskin [15] by approximately 30% on average.

关键词： NIST Approximation algorithms Recording Information and communication technology Cryptography Standards

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JADD-GAN: A Joint Attention Generative Adversarial Data Fusion Network for object Detection and Tracking

JADD-GAN: A Joint Attention Generative Adversarial Data Fusi...

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ieee international conference on High Performance computing and Communications (HPCC)

作者： Guoxia Xu Hao Wang Meng Zhao Hu Zhu Department of Computer Science Norwegian University of Science and Technology Gjøvik Norway School of Computer Science and Engineering Tianjin University of Technology Tianjin China School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China

Image fusion is the fusion of images captured by different sensors to generate a single image with enhanced information, and fusion technology, as one of the important branches in the field of information fusion, mainly realizes the processing of multi-source image information. However, many commonly used fusion methods usually ignore the visual naturalness and information fidelity of the fused images and lack emphasis on the salient information, which makes the fused images unsuitable for human visual perception. To address these shortcomings of existing methods, in this paper, we propose the Joint Attention and Dual Discriminator Generative Adversarial Data Fusion Network JADD-GAN. In the generator module, to increase the extraction of multi-level information by the network, we firstly adopt a dual encoder structure and give information fusion in the decoder part. Secondly, different discriminators are used for infrared and visible images in order to highlight the thermal radiation information and key textures. the effectiveness of the method is verified by experiments on four datasets, and the results show that the method can effectively highlight the thermal radiation information and key texture details of the fused images, fully demonstrating its great potential and performance in solving the infrared and visible images fusion (IVF) problem.

关键词： Visualization Data integration object detection Sensor fusion Feature extraction Generators Sensors

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An Improved Deep Network of Pulmonary Tuberculosis Lesions Detection based on YOLO

An Improved Deep Network of Pulmonary Tuberculosis Lesions D...

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ieee international conference on High Performance computing and Communications (HPCC)

作者： Le An Kexin Peng Xing Yang College of Computer Science and Cyber Security Chengdu University of Technology Chengdu China

Tuberculosis (TB) has been a potentially serious infectious lung disease for decades and remains the leading cause of death worldwide. Digital radiography (DR) imaging technology can locate and display suspicious areas of tuberculosis, and improve the quality of tuberculosis diagnosis. However, the tuberculosis lesions area has high compatibility with the background, the target has serious diffusion and the edge morphology is extremely irregular, which seriously interferes with the accuracy of diagnosis. Aiming at the above problems, this paper proposes a detection framework for pulmonary tuberculosis lesions based on a deep feature enhancement network. which is mainly composed of a deep-level feature enhancement module (DFEM) to strengthen the model to extract the deep semantic information of DR images. the experimental results show that on the TBX11K data set, the Average Precision (IOU=0.5) of the proposed detection framework reaches 82.9%, and the recall rate reaches 77.6 % . Compared with the YOLO series network, the model in this paper effectively improves the detection accuracy of pulmonary tuberculosis lesions and achieves accurate positioning and classification results.

关键词： Tuberculosis Shape Pulmonary diseases Semantics Lung Morphology object detection

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