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Lightweight Machine Sound Anomaly Detector Based on Parallel Discrete Wavelet Transform

IEEE SENSORS JOURNAL 2025年第10期25卷 18529-18542页

作者： Choi, Eunhye Park, Hyunggon Agcy Def Dev Daejeon 34186 South Korea Ewha Womans Univ Dept Elect & Elect Engn Seoul 03760 South Korea

The development of Industrial Internet of Things (IIoT) technology and network infrastructures has enabled the acquisition of substantial data, enabling data-driven condition monitoring and analysis. Detecting anomalies in machinery equipment is crucial in IIoT environments for safety enhancement, productivity, and reliability. To provide effective anomaly detection at IIoT edge nodes without delay, it is necessary to efficiently collect and process vast amounts of data from various sensors. While this demands a significant amount of computing resources, edge nodes only have limited data storage and processing capabilities. Therefore, our focus is on developing a lightweight anomaly detection algorithm for acoustic signal processing, considering the computational resources of the IIoT edge node. In this article, we propose the parallel discrete wavelet transform (PDWT) as an efficient method for compressing and processing acoustic signals received at edge nodes. This approach significantly alleviates memory consumption and reduces the computational time at the edge. In addition, by harnessing preprocessed features through PDWT, we can develop lightweight anomaly detection models suitable for deployment at the edge, making them highly practical for real-world implementation. The experimental results using real-world data collected from industrial machines confirm the effectiveness of the proposed solution.

关键词： Anomaly detection Feature extraction image edge detection Acoustics Industrial Internet of Things Computational modeling Training Autoencoders Transforms Discrete wavelet transforms Acoustic signal anomaly detection autoencoder discrete wavelet transform (DWT) edge computing Industrial Internet of Things (IIoT)

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An Efficient image-Based Virtual Scene Generation and Stitching Algorithm

An Efficient Image-Based Virtual Scene Generation and Stitch...

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2025 International conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Wei, Luo Chongqing Three Gorges Vocational College Wanzhou Chongqing400155 China

ISBN: (纸本)9798331509828

With the development of computer graphics and image processing, the virtual scene generation and splicing technology has been widely used in various fields of computer-aided image analysis. In this context, this study proposes a new and efficient image stitching algorithm to solve the image stitching artifacts caused by perspective changes and exposure differences in multi-camera systems, and expands it to the field of virtual images. First, as a pre-processing part, the model analyzes the image through nonlinear filtering and mesh deformation optimization to ensure image quality. Subsequently, the model presents a new image stitching algorithm based on the image matching method of minimizing projection deviation and thin plate spline theory. This algorithm further proposes an improved EIEOWF algorithm based on circular weighted fusion to achieve the task of smooth transition stitching of images. Finally, the stitching technology was extended to virtual scene generation, and OpenGL technology was used to achieve real-time 3D texture mapping. The test part is verified through experiments on the UDIS-D data set. The results show that the average splicing accuracy of the proposed algorithm in 100 sets of tests is 95%, with a standard deviation of 2%, which is significantly better than the existing comparison algorithm. © 2025 IEEE.

关键词： image matching

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image Recognition for Wildlife Conservation

Image Recognition for Wildlife Conservation

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2025 IEEE International conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Charanya, P. Sridharan, S. Yuvan Shankar, S. Sriram, M. Yashvanth, K.P. Department of Artificial Intelligence and Machine Learning Coimbatore India Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512965

This study adopts an empirical approach to evaluate the efficacy of image processing methods in conservation efforts for animals. The initial phase involves the collection of data from various sources within the natural ecosystem, including images captured through camera traps, drones, and other monitoring devices. This comprehensive and diverse dataset is then processed using advanced image processing Tools. A key strength of this methodology lies in its use of Machine Learning algorithms, particularly YOLO v8 (You Only Look Once) and Support Vector Machines (SVMs). These techniques are suitable for wildlife conservation because of their capacity to analyze complex visual data and detect patterns indicative of species presence, behavior, and habitat conditions. The paper outlines the application of YOLO v8 and SVMs to the collected image data, enabling accurate identification, real-time detection, and classification of wildlife species. This capability is essential for monitoring populations, tracking movements, and identifying potential threats, thus facilitating timely conservation actions. Furthermore, the research extends its evaluation by comparing the results of the Machine Learning approach with traditional ecological survey methods, offering a comprehensive assessment of the effectiveness of image processing in wildlife conservation. © 2025 IEEE.

关键词： Invertebrates

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Tropical Monsoon Cyclone Intensity Estimator using INSAT 3-D image 6

Tropical Monsoon Cyclone Intensity Estimator using INSAT 3-D...

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6th International conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Gilmary, Rosario Arunkumar, R. Kaviya, N. Information Technology IFET College of Engineering Villupuram India

ISBN: (纸本)9798331522667

Tropical cyclones (TC) are very destructive meteorological events that mostly affect coastal regions. For early warning and catastrophe management, TC intensity estimation is essential. One important source of real-time data for cyclone behavior prediction is satellite photography, especially that provided by INSAT-3D. Cyclone intensity may be precisely approximated by utilizing machine learning methods like as convolutional neural networks (CNNs) in conjunction with sophisticated image processing techniques like feature extraction and segmentation. The goal of this work is to develop a reliable model for cyclone strength estimates by integrating deep learning methods with INSAT-3D satellite data. The algorithm makes predictions based on visual characteristics such as wind speeds, sea surface temperatures, and cloud patterns. The suggested approach has the potential for real-time application and shows up to 95% accuracy in intensity prediction. By decreasing human interaction in TC estimates and increasing forecast accuracy, this method improves preparedness for disasters. © 2025 IEEE.

关键词： Tropical cyclone

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A Motion Injury Detection and Prevention System Based on image Recognition and Deep Learning 3

A Motion Injury Detection and Prevention System Based on Ima...

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3rd International conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Shao, Kang Zhang, Limin Ma, Yunlong Weifang Engineering Vocational College Shandong Weifang China

ISBN: (纸本)9798331508456

With the popularization of sports and fitness activities, how to effectively monitor and prevent sports injuries has become an important challenge. This article proposes a sports injury detection and prevention system. Firstly, this system collects real-time motion image data of athletes through high-definition cameras and uses YOLOv5 (You Only Look Once v5) for efficient object detection and action recognition. Then, combining long short-term memory networks (LSTM) to analyze time series data during the motion process. Finally, multimodal data fusion technology was introduced to comprehensively analyze the data. The experimental results show that the system achieves an accuracy of 88% when processing 5000 samples. In terms of real-time performance, the system has a response time of 0.60 seconds under 5000 samples. In the above data conclusions, the proposed system outperforms traditional models in terms of accuracy, real-time performance, and computational resource consumption in sports injury detection, demonstrating its effectiveness and feasibility in practical applications. © 2025 IEEE.

关键词： image fusion

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A Review on the Applications of Machine Learning and Deep Learning Algorithms for image Recognition 4

A Review on the Applications of Machine Learning and Deep Le...

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4th International conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Wanjari, Ketan Verma, Prateek Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha442001 India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Modern image recognition has experienced dramatic improvements because of Machine Learning and Deep Learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement while reviewing image recognition technologies extensively through literature. This paper demonstrates how the applications of healthcare and security systems and social media analysis influence society. New developments in technology have not resolved multiple unresolved obstacles such as data bias as well as computational complexity and privacy concerns together with real-time processing restrictions. The latest image processing techniques include ViTs alongside GANs and Few-Shot Learning but developers need to achieve better results in future improvements. The main goal of this research examines present-day advancements in ML and DL with a review of their capabilities as well as constraints before recommending future study paths to overcome problems encountered today. This paper evaluates both the future potential and benefits alongside drawbacks of ML and DL models applied to image recognition. © 2025 IEEE.

关键词： Adversarial machine learning

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Conservation of Wildlife in Hunting Tourism Using Artificial Intelligence and image processing in Smart Firearms 2nd

Conservation of Wildlife in Hunting Tourism Using Artificial...

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2nd International conference on Information Technologies and Their Applications

作者： Asil, Ufuk Nasibov, Efendi Dokuz Eylul Univ Izmir Turkiye

ISBN: (纸本)9783031734199;9783031734205

This paper presents an innovative technological approach to ensure the sustainability of state-controlled nature park hunting tourism and to protect wildlife. The developed smart fire control systems utilize artificial intelligence (AI) and advanced image processing techniques to prevent firing by activating the safety pin in various situations. Through an integrated camera module and a minicomputer from the Jetson family, animals can be detected in real-time, and various data analysis techniques, along with object recognition algorithms, allow for ethical intervention in many situations that are invisible to the human eye and could be exploited by hunters. Particularly in various national parks, the frequent reports of accidental or uninformed hunting of rare and endangered species, especially during their breeding seasons, underline the importance of this technology. Furthermore, hunters' preference for the most dominant animal in a herd as a trophy poses a significant threat in evolutionary terms. This technology contributes significantly to the conservation of wildlife while promoting ethical and sustainable practices in hunting tourism. The study addresses the development, testing, and implementation of this technology and adds a technological dimension to wildlife conservation strategies.

关键词： Artificial Intelligence image processing Smart Firearms Wildlife Conservation Hunting Tourism

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An efficient multi-modal fusion bird's-eye view 3D object detection algorithm 5

An efficient multi-modal fusion bird's-eye view 3D object de...

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5th International conference on Signal processing and Computer Science, SPCS 2024

作者： Zheng, Ke Li, Qingxia Zhao, Chunsheng Tian, Chuanyin CATARC Intelligent & Connected Technology Co. Ltd. Tianjin300393 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

At present, the perception method based on bird's-eye view has become the mainstream of autonomous driving perception. It realizes comprehensive perception of the vehicle's surrounding environment by fusing multiple sensors at the feature level. However, the existing multi-modal fusion perception methods based on bird's-eye view usually require extremely high computing resources, especially in the multi-camera view image conversion processing. In addition, the key to multi-modal bird's-eye view perception lies in how to efficiently fuse point cloud features and image features. To address these defects, this paper proposes a novel multi-modal bird's-eye view perception algorithm. First, this paper proposes an index lookup calculation method for the conversion of multi-view image features to bird's-eye view perspective. This method greatly reduces the consumption of computing resources without basically reducing information. Secondly, this paper proposes a feature fusion method, which uses a cross-modal attention mechanism to enhance the interaction between different modal features, realize dynamic spatiotemporal alignment and fusion. Experimental results show that the method proposed in this paper can effectively perceive the environment and can be deployed on a real vehicle platform for real-time detection. © 2025 SPIE.

关键词： image enhancement

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Depth-Aware Weather-Enhanced 3D Object Detection for Roadside Infrastructure 8

Depth-Aware Weather-Enhanced 3D Object Detection for Roadsid...

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8th International conference on Video and image processing, ICVIP 2024

作者： Li, Yanfei Zeng, Yuan Gong, Yi Department of Electronics and Electrical Engineering Southern University of Science and Technology China College of Big Data and Internet Shenzhen Technology University China

ISBN: (数字)9781510689244

ISBN: (纸本)9781510689237

Monocular 3D object detection is a crucial topic in autonomous driving and Intelligent transportation systems (ITS). Most existing methods are evaluated on clean datasets but exhibit arresting performance degradation in the real world with varying weather conditions. image enhancement methods have been introduced in existing vehicle-centric works to address the impact of diverse weather on detection performance and improve the robustness of the model. Unlike vehicle-centric systems, roadside infrastructure can enlarge perception range and is less affected by vehicle occlusion to increase the response time in case of danger. To enhance the robustness of detection models on roadside infrastructure, this work introduces an image enhancement method incorporating depth information in modeling climate changes. We use a classification network trained on real weather data to validate the effectiveness of the enhancement and use state-of-the-art roadside infrastructure monocular detection models to evaluate the effectiveness of our method on object detection. Extensive experiments demonstrate that our method can enhance the robustness and generalization of detection models under various weather conditions. © 2025 SPIE.

关键词： Object detection Rain image enhancement Data modeling Education and training Cameras 3D modeling Adverse weather Performance modeling image contrast enhancement

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real time processing and analysis of truck blind spot images based on computer vision

Real time processing and analysis of truck blind spot images...

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2024 International conference on Physics, Photonics, and Optical Engineering, ICPPOE 2024

作者： Dong, Jinsong Research Institute of Highway Ministry Transport Beijing100088 China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

Due to factors such as the large body volume, high position of the driver's cabin, and limited range of rearview mirror reflection, trucks have blind spots when making right turns and reversing. This prevents drivers from fully assessing the surrounding environment, leading to safety incidents. This paper proposes a computer vision-based method for detecting obstacles in truck blind spots. Deep learning-based detection techniques, particularly the YOLO series algorithms, are commonly used due to their simplicity and speed in real-time detection. However, inaccuracies in output bounding boxes, especially in detecting small-scale and overlapping objects, lead to low detection accuracy. This paper improves upon the YOLOv5s framework by incorporating attention mechanisms to enhance the feature extraction capability for small-scale and overlapping objects. Additionally, to prevent image feature loss and resolution reduction caused by pooling operations, multiple parallel dilated convolutions with different dilation rates are used instead of pooling layers, forming an atrous pyramid pooling structure. This structure is added after different hierarchical feature outputs and fused to increase the network's receptive field. © 2025 SPIE.

关键词： Trucks

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