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检索条件"主题词=detection Algorithms"

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Improvement of Multi-Scale Ship detection Algorithm for SAR Images Based on YOLOv7

Improvement of Multi-Scale Ship Detection Algorithm for SAR ...

引用

Smart Grid and Artificial Intelligence (SGAI), International Conference on

作者： Ming Ling Ying Zhang Shanghai Maritime University Shanghai China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

In recent years, deep learning, especially Convolutional Neural Networks (CNNs), has made significant progress in ship target detection in Synthetic Aperture Radar (SAR) images. However, existing methods still face challenges such as missed detections and false positives when dealing with complex maritime scenes and multi-scale ship targets. To address these issues, this paper proposes an improved YOLOv7 model—ACF-YOLOv7, focusing on multi-scale ship detection in complex environments. This study designs an innovative CB-BiFPN module that combines the Bidirectional Feature Pyramid Network (BiFPN) and Convolutional Block Attention Module (CBAM). By introducing a learnable weighted mechanism, this module efficiently fuses features, enhances the focus on key features, and suppresses redundant information. It effectively reduces feature loss for small ships and fully integrates ship target feature maps across different scales. Additionally, the paper introduces the SIoU loss function to further optimize target localization accuracy. Experiments on the SSDD dataset show that ACF-YOLOv7 achieves improvements of 2.6%, 1.8%, and 3.5% in detection accuracy (94.11%), recall rate (92.24%), and mean average precision (mAP), respectively, compared to the baseline YOLOv7. Comparative experiments also demonstrate that ACF-YOLOv7 outperforms other deep learning methods in complex scenarios. This research provides an efficient and robust solution for ship target detection in SAR images in complex environments.

关键词： Deep learning Location awareness Accuracy Focusing Object detection Radar polarimetry Smart grids Marine vehicles detection algorithms Synthetic aperture radar

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GCE-YOLO: A Lightweight Fire detection Algorithm Optimized for Edge Devices

GCE-YOLO: A Lightweight Fire Detection Algorithm Optimized f...

引用

Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Yuchen Zheng Junjie Lu Wenzao Shi Liwei Guan Yuping Wu College of Photonic and Electronic Engineering Fujian Normal University Fuzhou China College of Physics and Energy Fujian Normal University Fuzhou China

ISBN: (数字)9798331522285

ISBN: (纸本)9798331522292

In recent years, deep learning based video fire detection methods have been widely adopted in intelligent security systems. However, existing algorithms often exhibit high computational complexity, limiting their feasibility for deployment on edge devices. To overcome this challenge, this paper introduces GCE-YOLO, a lightweight fire detection algorithm based on YOLOv8, designed to enhance efficiency while maintaining detection accuracy. Firstly, the GTFormer module is introduced into the backbone network, integrating the global modeling capability of Transformer with the local feature extraction advantages of CGLU, significantly improving model accuracy and multi-scale fire feature representation. Secondly, a lightweight cross-scale feature fusion module (CCFM) is designed, which reduces model parameters and computational complexity by unifying the channel dimensions in the neck network. Lastly, the EIoU loss function is adopted in place of CIoU for bounding box regression optimization, enhancing the model's adaptability to dynamic changes in fire size by incorporating constraints on center point distance and aspect ratio. Experimental results show that GCE-YOLO achieves an mAP @ 0.5 of 88.6 % and a Precision of 90.6 %, while maintaining a lightweight architecture with only 1.7M Parameters. These findings highlight its efficiency and suitability for fire detection on resource-constrained edge devices.

关键词： Adaptation models Accuracy Computational modeling Image edge detection Feature extraction Neck detection algorithms Computational complexity Optimization Edge computing

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A Study on Automated Tilt Control for a Tipper System Using RGB-D Camera-Based Cargo detection

A Study on Automated Tilt Control for a Tipper System Using ...

引用

International Conference on Electronics, Information and Communications (ICEIC)

作者： JeonMin Kang SuHong Eom Jin Soo Kim Eung Hyuk Lee Dept of Electronic Engineering Tech University of Korea Siheung-si South Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

This paper proposes a rolltainer unloading system incorporating a tipper mechanism and RGB-D camera to address challenges in unloading diverse cargo types in logistics environments. While conventional unloading robots require repetitive object recognition and manipulation, our system enables batch unloading without pre-trained object recognition algorithms, effectively handling cargo of various sizes and shapes, making it adaptable to diverse logistics scenarios. The system measures the maximum height of loaded cargo through an RGB-D camera and automatically adjusts the tipper system's tilt angle accordingly for batch unloading. Dynamic tilt adjustment of the tipper system facilitates complete unloading by addressing any remaining cargo.

关键词： Shape Heuristic algorithms Robot vision systems Cameras Discharges (electric) Object recognition detection algorithms Robots Logistics

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LE-YOLO: Lightweight and Efficient Algorithm for Aerial Small Target detection

LE-YOLO: Lightweight and Efficient Algorithm for Aerial Smal...

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Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Shuai Gao Linbo Zhang Tingwei Zhu College of Information and Communication Engineering Harbin Engineering University Harbin China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Aiming at the problems of limited edge equipment resources and poor effectiveness of small target recognition in aerial images, a lightweight and efficient algorithm LE-YOLO is proposed based on the baseline model YOLO11s. Firstly, a minimal target detection head is added to improve the detection precision by using the P2 layer feature with higher resolution. Then, a lightweight asymmetric detection head (LA-Head) is designed, and depthwise separable convolution is used to reduce the amount of model parameters and calculations. Finally, a lightweight and faster feature extraction architecture (LF - Bottleneck) is designed, which combines gating mechanism and partial convolution to design bottleneck architecture to further reduce the number of model parameters and computation. Experiments on the public dataset VisDrone 2019 show that compared with YOLO11s, mAP50 is increased by 2.1% and mAP50-95 is increased by 1.3% under the premise that parameters and calculations are reduced by 24.9% and 4.9% respectively.

关键词： Head Image resolution Target recognition Convolution Image edge detection Computational modeling Object detection Computer architecture Feature extraction detection algorithms

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Research on Traveling Wave Head detection Algorithm for Wavelet Multi-Scale Information Fusion

Research on Traveling Wave Head Detection Algorithm for Wave...

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Advances in Electrical, Electronics and Computing Technology (EECT), International Conference on

作者： Jiahao Lu Chuyang Liu Tong Wu Kaiqi Zhang Jielong Chen Sizhe Chen Foshan Power Supply Bureau Guangdong Power Grid Co Ltd. Foshan China School of Automation Guangdong University of Technology Guangzhou China

ISBN: (数字)9798331541545

ISBN: (纸本)9798331541552

A wavelet transform multi-scale information synthesis algorithm for traveling wave head detection is proposed. Firstly, the fault traveling wave signal is processed using the multi-scale analysis of wavelet transform to obtain the modulus maximum values of wavelet coefficients at various scales. Then, the modulus maximum values are sequentially calibrated in a parallel coordinate system to form a grayscale image. Finally, a convolutional neural network is used to build a wave head detection model. This algorithm cleverly links wavelet transform with convolutional neural network using a parallel coordinate system, combining information from wavelet transform at multiple scales, providing a new approach for detecting traveling wave heads.

关键词： Wavelet transforms Computers Head Gray-scale Wavelet analysis Convolutional neural networks detection algorithms Wavelet coefficients

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Analysis of Automatic detection Algorithm for Network UI Design Violations Based on Unity3D

Analysis of Automatic Detection Algorithm for Network UI Des...

引用

International Conference on Communication Systems and Network Technologies (CSNT)

作者： Zhen Li Shandong Vocational College of Science and Technology Weifang China

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

The network UI design based on the Unity3D engine faces design violations such as unreasonable interface layout, non-compliant interactive operations, and potential interface element errors. This paper proposes a UI violation detection algorithm based on the Unity3D engine. The algorithm first traverses the UI tree structure of the Unity3D scene, starting from the root node and going deeper layer by layer to obtain all UI objects and their component information one by one. Next, specific and detailed violation detection rules are set according to the UI design specifications. At the same time, in order to detect interactive operations more accurately, a ray detection algorithm module is introduced to simulate user operations and detect various interaction modes of UI objects, such as ray interaction, trigger interaction, and field of view. Through the ray detection algorithm, it detects whether the button is clicked correctly and whether the drag operation is smooth. In the violation determination stage, the information of each UI object is compared one by one with the preset violation detection rules. A comparison found that the algorithm is superior to traditional manual detection methods in terms of detection speed and accuracy. The average detection time is shortened to 1.68 seconds and the accuracy is as high as 99.1%. In addition, the detection efficiency and accuracy of the algorithm are further improved by integrating feedback learning mechanism, modular design and optimizing algorithm performance. This study not only provides a new automated method for network UI design violation detection, but also verifies its effectiveness in practical applications through experimental data.

关键词： Learning systems Accuracy Machine learning algorithms Layout Manuals Machine learning Object recognition detection algorithms Engines Optimization

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Accelerometer-Based Person-in-Bed detection Challenge

Accelerometer-Based Person-in-Bed Detection Challenge

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Lauren Mentzer Ravi Kiran Raman Atulya Yellepeddi Analog Devices Inc.

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In-bed, non-contact vital signs monitoring enables patient monitoring without the use of intrusive sensors that interfere with patients’ sleep. Accelerometers incorporated into mattresses can be used to estimate insights related to vitals and sleep quality when someone lays on the mattress. A necessary first step to this is detecting whether someone is lying on the bed. In this signal processing grand challenge, we consider the problem of person-in-bed detection using only 3-axis accelerometer data obtained using the ADXL355 sensor. In particular, we focus on low-cost, low-latency detection algorithms through two challenge tracks, the first focused on detection over limited windows and the second on state tracking aimed to detect changes quickly.

关键词： Accelerometers Patient monitoring Signal processing Acoustics Sensors Speech processing Low latency communication detection algorithms

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TFFM-YOLO: An Efficient High-Altitude Multi-Target detection Algorithm on UAV Infrared Photography

TFFM-YOLO: An Efficient High-Altitude Multi-Target Detection...

引用

Smart Grid and Artificial Intelligence (SGAI), International Conference on

作者： Chang Sun Chengxuan Li School Of Electrical Engineering and Electronic Information Xihua University Chengdu China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

Infrared detection and tracking have emerged as pivotal aspects in the domain of object detection in recent years. However, detecting small objects continues to pose a significant challenge due to their diminutive size and the tendency for indistinct feature details to result in their omission. Moreover, information pertaining to small objects is often lost during downsampling operations. Deep learning-based detection methods have been employed to address these challenges. In this work, we propose an efficient aerial target detection algorithm, TFFM-YOLO, designed to increase detection accuracy and enhance image details within convolutional networks. Firstly, we integrate an Enhanced Receptive Field Augmentation Module (ERFB) into the backbone, aiming to mitigate background interference and amplify target features. Furthermore, we have developed a Triple Feature Fusion Module (TFFM) while leveraging three feature maps from different levels for additional fusion before their input into the detection network. Finally, the normalized Wasserstein distance is employed to optimize the balance of small target thresholds, thereby enhancing the probability of accurate bounding box detection. On the Hit-UAV dataset, our method achieves an 11.5% improvement in mAP@0.5 with minimal changes in the number of parameters compared to the original algorithm.

关键词： YOLO Photography Thermal expansion Accuracy Interference Feature extraction Autonomous aerial vehicles Smart grids Convolutional neural networks detection algorithms

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Recurrence-Based Vanishing Point detection

Recurrence-Based Vanishing Point Detection

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Skanda Bharadwaj Robert T. Collins Yanxi Liu School of EECS The Pennsylvania State University University Park PA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Classical approaches to Vanishing Point detection (VPD) rely solely on the presence of explicit straight lines in images, while recent supervised deep learning approaches need labeled datasets for training. We propose an alternative unsupervised approach: Recurrence-based Vanishing Point detection (R-VPD) that uses implicit lines discovered from recurring correspondences in addition to explicit lines. Furthermore, we contribute two Recurring-Pattern-for-Vanishing-Point (RPVP) datasets: 1) a Synthetic Image dataset with 3,200 ground truth vanishing points and camera parameters, and 2) a Real-World Image dataset with 1,400 human annotated vanishing points. We compare our method with two classical methods and two state-of-the-art deep learning-based VPD methods. We demonstrate that our unsupervised approach outperforms all the methods on the synthetic images dataset, outperforms the classical methods, and is on par with the supervised learning approaches on real-world images. Code and data can be found here: http://***/data/***

关键词： Deep learning Training Computer vision Codes Urban areas Supervised learning Cameras Feature extraction detection algorithms Unsupervised learning

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Using Geolocalized Social Network for Contact Tracing Based on Community detection

Using Geolocalized Social Network for Contact Tracing Based ...

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iNnovative Informatics of Biskra (ISNIB), International Symposium on

作者： Faiza Deghmani Chahinez Benaries Hassina Aliane Kamel Boukhalfa DRDHN CERIST Algiers Algeria Faculty of Computer Science USTHB Algiers Algeria National Sanitary Security Agency National School of Artificial Intelligence Algiers Algeria

ISBN: (数字)9798331509576

ISBN: (纸本)9798331509583

Contact tracing is a method that traces infected people and then identifies all their contacts defined as potential new cases. In response to sudden outbreaks of epidemics, many contact tracing systems were proposed. Despite their efficiency, most of these systems are limited. While mobile applications require users' cooperation to install them, systems based on online social networks are more appropriate for communities active on online platforms. However, Google Maps with over twenty million downloads, has recently evolved into a social network in disguise where users share their opinions about the places they have visited. Thus, Google Maps data offer a good substitute for conventional contact tracking data. In this paper, we propose a novel approach applying a community detection algorithm to a social network built on Google Maps data. The proposed approach uses the Louvain algorithm to group both infected and safe people in communities, then identifies suspected cases based on resulted communities. Finally, the implementation of our approach shows its efficiency in covering more suspected cases compared to similar work.

关键词： Photography Social networking (online) Web services Geology Soft sensors Contact tracing Robustness Satellite images detection algorithms Synthetic data

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