检索结果-内蒙古大学图书馆

19th IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Wang, Hui Wang, Zhiqiang Yu, Lijun He, Xinting Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China

ISBN: (数字)9781665408530

ISBN: (纸本)9781665408530;9781665408523

Aiming at the problems of insufficient detection accuracy of small targets and insufficient receptive field of feature points in the field of target detection, a yolo target detection algorithm with hybrid atrous convolution pyramid is proposed. The algorithm firstly introduces atrous convolutions with different dilation rates into the feature pyramid network, and builds a hybrid receptive field module (HRFM), which enhances the ability to obtain global information by increasing the receptive field, and solves the problem of target occlusion;Secondly, design a pyramid network of atrous path aggregation, fusion of shallow feature information and high-level semantic information, improve the global detail information and representation ability of feature maps, and enhance the multiscale adaptability of the model. Three progressive schemes are designed for testing on the VOC dataset. The experimental results show that the algorithm can effectively solve the problem of target occlusion and improve the detection accuracy of small targets.

关键词： Atrous convolution Receptive Field yolo algorithm Feature Pyramid

来源：评论

学校读者我要写书评

暂无评论

The improvement in obstacle detection in autonomous vehicles using yolo non-maximum suppression fuzzy algorithm

引用

JOURNAL OF SUPERCOMPUTING 2021年第11期77卷 13421-13446页

作者： Zaghari, Nayereh Fathy, Mahmood Jameii, Seyed Mahdi Shahverdy, Mohammad Islamic Azad Univ Dept Comp Engn Shahr E Qods Branch Tehran Iran Inst Res Fundamental Sci Sch Comp Sci Tehran Iran IRAN Univ Sci & Technol IUST Dept Comp Engn Tehran Iran

Numerous changes in algorithms have been observed by object detection to enhance both speed and accuracy. In this research, we present a method to improve the behavioral clone of self-driving cars. Thus, we first create a collection of videos and information required for safe driving on different routes and conditions. The detection of obstacles is done with the proposed algorithm called "yolo non-maximum suppression fuzzy algorithm, which performs the driver reaction to obstacles with greater accuracy and more speed than the obstacles detection algorithms using the designed framework. The network is trained by the driver's performance, and hence, the output used to control the vehicle is obtained. The non-maximum suppression algorithm plays an essential role in object detection and tracking. An effective hybrid method of fuzzy and NMS algorithms is provided in this paper to improve the problem mentioned. The proposed method improves the average accuracy of the detection network. The performance of the designed algorithm was examined using two different types of KITTI data and the data collected using the personal vehicle and the data we gathered. The proposed algorithm was assessed with evaluation accuracy criteria, which revealed that the method has a higher speed (above 64.41%), a lower FPR (below 6.89%), and a lower FNR (below 3.95%) compared with the baseline yolov3 model. According to the loss function, the accuracy rate of the network performance is 95%, implying that we have achieved good results.

关键词： Self-driving Object detection yolo algorithm Non-maximum suppression algorithm Fuzzy Convolutional neural network (CNN)

来源：评论

学校读者我要写书评

暂无评论

TPGR-yolo: Improving the Traffic Police Gesture Recognition Method of yolov11

引用

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第2期16卷 404-415页

作者： Qi, Xuxing Xu, Cheng Liu, Yuxuan Ma, Nan Liu, Hongzhe Beijing Union Univ Beijing Key Lab Informat Serv Engn Beijing Peoples R China Beijing Univ Technol Coll Informat & Technol Beijing Peoples R China Beijing Qiangqiang Yuanqi Technol Co Ltd Beijing Peoples R China

In open traffic scenarios, gesture recognition for traffic police faces significant challenges due to the small scale of the traffic police and the complex background. To address this, this paper proposes a gesture recognition network based on an improved yolov11. This method enhances feature extraction and multi-scale information retention by integrating RFCAConv and C2DA modules into the backbone network. In the Neck part of the network, an edge-enhanced multi-branch fusion strategy is introduced, incorporating target edge information and multiscale information during the feature fusion phase. Additionally, the combination of WIoU and SlideLoss loss functions optimizes the positioning of bounding boxes and the allocation of sample weights. Experimental validation was conducted on multiple datasets, and the proposed method achieved varying degrees of improvement in all metrics. Experimental results demonstrate that this method can accurately perform the task of recognizing traffic police gestures and exhibits good generalization capabilities for small targets and complex backgrounds.

关键词： Traffic police gesture recognition loss function yolo algorithm multi-scale feature fusion

来源：评论

学校读者我要写书评

暂无评论

yolo Target Detection algorithm with Deformable Convolution Kernel 18

YOLO Target Detection Algorithm with Deformable Convolution ...

引用

IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Wang, Hui Zhang, Shuai Yu, Lijun Shi, Ce Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China

ISBN: (纸本)9781665441018

In order to solve the problem of inaccurate detection of targets with large scale and shape changes and small targets by the yolo target detection algorithm, a yolo target detection algorithm with deformable convolution kernel is proposed. The algorithm adds deformable convolution to the network, and designs three progressive embedding schemes, so that the network can adaptively change the receptive field of feature points according to the shape of the target, thereby extracting features more effectively and improving detection accuracy;by adjusting the backbone network the structure reduces the calculation amount of the algorithm. Tested on the VOC data set, the results show that the algorithm can effectively improve the accuracy of target detection, and the detection speed meets the real-time requirements.

关键词： yolo algorithm Deformable Convolution Receptive Field Target Detection

来源：评论

学校读者我要写书评

暂无评论

Vehicle Detection Method in Foggy Weather Based on yolo with Human Eye Mechanisms 17th

Vehicle Detection Method in Foggy Weather Based on YOLO with...

引用

17th International Conference on Intelligent Robotics and Applications

作者： Shao, Quanzhi Univ Calif Davis Davis CA 95616 USA

ISBN: (纸本)9789819607884;9789819607891

Challenging weather conditions such as fog pose a big problem in vehicle detection because it hinders easy recognition and affects the quality of the image even more. In this work, vehicle detection is investigated with an enhancement of accuracy for poor lighting conditions through the employment of yolo deep learning algorithm coupled with Retinex image enhancement technique. yolo is an advanced object detection algorithm that is fast and very precise but is not sensitive to low light areas. Likely to improve the image visibility and detection reliability by mimicking mechanisms of a human eye, the Retinex algorithm is capable to mitigate the negative impact of fog. The overall idea presented in this paper is based on the pipelined approach, where Retinex algorithm will be employed initially to improve the input images to be fed to the yolo vehicle detection system. The effectiveness of this approach is tested using a set of foggy weather images revealing much higher level of detection performance and precision as compared to yolo alone.

关键词： Vehicle detection Foggy conditions Poor lighting yolo algorithm Detection accuracy Visibility improvement Mechanisms of a human eye

来源：评论

学校读者我要写书评

暂无评论

Automated steel corrosion detection and damage assessment using yolov5 algorithm

引用

SHIPS AND OFFSHORE STRUCTURES 2025年

作者： Mu, Jianwu Yang, Yang Chu, Jinze Lv, Wanyang Hu, Kaixuan Dalian Univ Technol Sch Naval Architecture & Ocean Engn Dalian Liaoning Peoples R China

Current ship corrosion inspections rely heavily on manual observation, which is subjective, inefficient, and difficult in inaccessible areas. This paper proposes a method for identifying steel corrosion and determining damage levels using the yolov5 (You Only Look Once, Version 5) algorithm and its enhancements. The method increases the steel corrosion dataset and applies yolo for corrosion image recognition. Converting images to greyscale reduces the impact of colour on corrosion detection, while Gaussian filtering eliminates noise. A segmented linear transformation enhances contrast between corroded and non-corroded areas. To detect corrosion, a Otsu's method is used, and expansion operations reduce noise in small corrosion regions. The 8-neighborhood method then counts and analyses corrosion areas, providing details on number, size, and location. By combining these techniques with the DOP (Degree of Pitting Corrosion) index and damage classification, the method quantitatively evaluates corrosion damage on ship surfaces. Significantly improving detection efficiency and accuracy.

关键词： Corrosion damage identification convolutional neural networks yolo algorithm feature extraction

来源：评论

学校读者我要写书评

暂无评论

Enhancing autonomous pavement crack detection: Optimizing yolov5s algorithm with advanced deep learning techniques

引用

MEASUREMENT 2025年 240卷

作者： Zhou, Shuangxi Yang, Dan Zhang, Ziyu Zhang, Jinwen Qu, Fulin Punetha, Piyush Li, Wengui Li, Ning Guangzhou Maritime Univ Sch Civil & Engn Management Guangzhou 510725 Peoples R China East China Jiaotong Univ Sch Civil Engn & Architecture Nanchang 330013 Peoples R China Univ New South Wales Ctr Infrastruct Engn & Safety Sch Civil & Environm Engn Sydney NSW 2052 Australia Univ Technol Sydney Sch Civil & Environm Engn Ultimo NSW 2007 Australia Univ Manchester Dept Solids & Struct Manchester M13 9PL England

To enhance the safety and comfort of vehicle travel, detecting pavement cracks is a critical task in road management. This article introduces an advanced single-stage target detection method utilizing the yolov5s algorithm to enhance real-time performance and accuracy. Initially, Squeeze-and-Excitation Networks are integrated into the model to facilitate self-learning for improved crack characterization. Subsequently, anchors computed through the K-means clustering algorithm are closely aligned with the fracture dataset, achieving an adaptation rate of 99.9 % and enhancing the recall rate of the model. Furthermore, the inclusion of the SimSPPF module from yolov6 diminishes memory usage and expedites detection speed. By replacing the original nearest up- sampling method with transposed convolution, optimization of up-sampling for crack datasets is achieved. Performance assessments reveal that the refined yolov5s algorithm attains an F1 score of 91 %, a mean Average Precision (mAP) of 93.6 %, and a 1.54 % increase in frames per second (fps) for pavement crack detection. This enhancement in detection technology signifies a substantial advancement in the maintenance and longevity of road infrastructure.

关键词： Road maintenance Crack detection yolo algorithm AI integration Advanced image processing

来源：评论

学校读者我要写书评

暂无评论

Detection of abandoned objects based on yolov9 and background differencing

引用

SIGNAL IMAGE AND VIDEO PROCESSING 2025年第1期19卷 1-8页

作者： Song, Huajun Wang, Jinbo Zhang, Yunze China Univ Petr East China Coll Oceanog & Space Informat Changjiang West Rd Qingdao 266580 Shandong Peoples R China

Abandoned objects on highways can cause serious traffic accidents. Abandoned objects detection algorithms are of great significance in reducing traffic accidents and improving the efficiency of highway management. Existing traditional image processing detection algorithms are difficult to detect small targets in the complex and changing highway environment. And the current deep learning methods are limited by the number of samples and the influence of vehicle occlusion resulting in lower detection accuracy. Aiming at the above problems, a abandoned object detection algorithm for highway is designed by combining the background modeling algorithm MOG2 and the yolov9 detector. Utilizing the difference of the two algorithms on the recognition mechanism of abandoned objects, the results of the yolov9 detector are used as a guide to make a comprehensive judgment on the low confidence targets combined with the iterative difference results of MOG2. The actual test results on highways show that the abandoned objects detection algorithm based on yolov9 and MOG2 has an improved precision and recall rate for abandoned objects detection compared to a single detection algorithm, with an average precision of 86.7%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document} and a recall rate of 89.6%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}. The algorithm effectively complements the existing abandoned objects detection algorithms and has engineering practical value.

关键词： Abandoned object detection Computer vision yolo algorithm Gaussian mixture model

来源：评论

学校读者我要写书评

暂无评论

Enhancing Bolt Object Detection via AIGC-Driven Data Augmentation for Automated Construction Inspection

引用

BUILDINGS 2025年第5期15卷 819-819页

作者： Wu, Jie Han, Beilin Zhang, Yihang Huang, Chuyue Qiu, Shengqiang Feng, Wang Liu, Zhiwei Zou, Chao Wuhan Polytech Univ Sch Civil Engn & Architecture Wuhan 430023 Peoples R China Guangdong Prov Key Lab Intelligent Disaster Preven Dongguan 523808 Peoples R China Wuhan Univ Technol Sch Transportat & Logist Engn Wuhan 432063 Peoples R China Cent & Southern China Municipal Engn Design & Res Wuhan 430010 Peoples R China Guangdong Univ Technol Sch Civil & Transportat Engn Guangzhou 510006 Peoples R China

In the engineering domain, the detection of damage in high-strength bolts is critical for ensuring the safe and reliable operation of equipment. Traditional manual inspection methods are not only inefficient but also susceptible to human error. This paper proposes an automated bolt damage identification method leveraging AIGC (Artificial Intelligence Generated Content) technology and object detection algorithms. Specifically, we introduce the application of AIGC in image generation, focusing on the Stable Diffusion model. Given that the quality of bolt images generated directly by the Stable Diffusion model is suboptimal, we employ the LoRA fine-tuning technique to enhance the model, thereby generating a high-quality dataset of bolt images. This dataset is then used to train the yolo (You Only Look Once) object detection algorithm, demonstrating significant improvements in both accuracy and recall for bolt damage recognition. Experimental results show that the LoRA fine-tuned Stable Diffusion model significantly enhances the performance of the yolo algorithm, providing an efficient and accurate solution for automated bolt damage detection. Future work will concentrate on further optimizing the model to improve its robustness and real-time performance, thereby better meeting the demands of practical industrial applications.

关键词： high-performance bolts damage detection automated detection AIGC technology yolo algorithm

来源：评论

学校读者我要写书评

暂无评论

Improved small foreign object debris detection network based on yolov5

引用

JOURNAL OF REAL-TIME IMAGE PROCESSING 2024年第1期21卷 21-21页

作者： Zhang, Heng Fu, Wei Li, Dong Wang, Xiaoming Xu, Tengda Xihua Univ Sch Comp & Software Engn Chengdu Peoples R China Chengdu Univ Coll Chinese & ASEAN Arts Chengdu Peoples R China

In response to the challenges of detecting foreign object debris (FOD) on airport runways, where the objects are small in size and have indistinct features leading to false detections and missed detections, significant improvements were made to the yolov5 algorithm. First, the original yolov5-n model was optimized by incorporating multi-scale fusion and detection enhancements. To improve detection speed and reduce parameters, the detection head for large objects was removed. Second, the C3 module in the backbone network was replaced with the C2f module, resulting in enhanced gradient flow and improved feature representation capabilities. Additionally, the spatial pyramid pooling-fast (SPPF) module in the backbone network was refined to expand the receptive field and enhance the model's perception of dependencies between targets and backgrounds. Furthermore, the coordinate attention (CA) mechanism was introduced in the neck layer to further enhance the model's perception of small FOD items. Lastly, the SCYLLA-IoU (SIoU) loss function was introduced to further improve the speed and accuracy of bounding box regression. Moreover, the nearest neighbor interpolation upsampling method was substituted with the lightweight Content-Aware ReAssembly of FEatures (CARAFE) upsampling operator to better exploit global information. Experimental results on the Fod_Tiny dataset, which consists of small FOD items in airports, demonstrated a significant 5.4% improvement over the baseline algorithm. To validate the generalizability of the algorithm, experiments were conducted on the Mirco_COCO dataset, resulting in a notable 1.9% improvement compared to the baseline algorithm.

关键词： FOD Object detection yolo algorithm Deep learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：