检索结果-内蒙古大学图书馆

Lightweight improved yolov5 algorithm for PCB defect detection

JOURNAL OF SUPERCOMPUTING 2025年第1期81卷 1-21页

作者： Xie, Yinggang Zhao, Yanwei Beijing Informat Sci & Technol Univ Key Lab Informat & Commun Syst Minist Informat Ind Beijing 100101 Peoples R China Beijing Informat Sci & Technol Univ Key Lab Minist Educ Optoelect Measurement Technol Beijing 100101 Peoples R China

A lightweight yolov5 improved algorithm-based inspection model is proposed to address the problems of defective printed circuit boards (PCBs), which are difficult to identify. First, the detection part of yolov5 is changed to dual-head detection to significantly improve the inference speed of the model on edge devices and adapt to the real-time target detection requirements. Second, the introduction of GSConv in the Neck part helps to further reduce the number of parameters of the model and improve the computational efficiency, which can enhance the model's capture ability. Finally, BiFPN is introduced to fuse multi-scale information to enhance the model's detection ability for targets of different sizes. The experimental results show that the improved lightweight yolov5 algorithm in this paper achieves 94.9% in the average accuracy mean (mAP@0.5), which is only 0.5 percentage points less compared to the original yolov5 algorithm. However, the improved algorithm has 56.2% fewer floating point operations (GFLOPs) and 53.7% fewer parameters. This improvement not only makes the algorithm more accurate and lightweight, but also significantly improves the efficiency of PCB inspection, which better meets the needs of industrial production.

关键词： yolov5 algorithm GSConv Detection layer improvement BiFPN Attention mechanism

来源：评论

学校读者我要写书评

暂无评论

Carbonate Rock Fracture Identification Method Based on an Improved yolov5 algorithm

引用

PURE AND APPLIED GEOPHYSICS 2024年第1期181卷 189-201页

作者： Xie, Jun Gao, Renjie Zhang, Yuanpei Zhang, Jianguo Xia, Yong He, Yilin Shandong Univ Sci & Technol Coll Earth Sci & Engn Qingdao 266590 Peoples R China Changqing Oilfield Explorat & Dev Res Inst Xian 710012 Peoples R China

Fractures play a crucial role in discovering and developing petroleum reserves. However, traditional logging techniques face significant challenges in identifying fractures. To address such challenges, this article proposes a new method that combines conventional logging data with a small amount of marker data from cores and image logs to identify fracture development in reservoirs with high accuracy and fast operation. The proposed method is based on the improved yolov5, which offers a new idea for fracture identification. The fracture data from the carbonate rocks of the Sulige gas field in the Ordos Basin were used for training and validation. Finally, positive experimental outcomes were achieved, demonstrating the usefulness of the improved yolov5 algorithm in detecting fracture development in carbonate rocks.

关键词： Fracture recognition deep learning yolov5 algorithm

来源：评论

学校读者我要写书评

暂无评论

Garbage Detection and Classification Method based on yolov5 algorithm 14

Garbage Detection and Classification Method based on YoloV5 ...

引用

14th International Conference on Machine Vision (ICMV)

作者： Lv, Zhaohao Li, Huiyan Liu, Yeming Tianjin Univ Technol & Educ Tianjin Peoples R China

ISBN: (数字)9781510650459

ISBN: (纸本)9781510650459;9781510650442

In the face of a wide variety and a large number of production and domestic waste, it is a great challenge for the task of automatic detection and sorting of waste. Based on yolov5 algorithm, this paper proposes a method for rapid detection and classification of garbage, trains the model on taco[1] garbage data set, and extracts the location and feature information of garbage through this network model according to the experimental results. In reality, this model can effectively detect the garbage classified by the data set. After testing, the mAP(Mean Average Percision) value of the model reaches 97.62%, the detection accuracy is 95.49%, and the detection speed reaches 5.52fps. Compared with yolov3 network model, which better complete the task of garbage classification and detection. This network model has the necessary technical conditions for the algorithm of waste sorting robots.

关键词： Garbage classification Object detection Deep learning yolov5 algorithm

来源：评论

学校读者我要写书评

暂无评论

Research on traffic light target detection method based on improved yolov5 algorithm 7

Research on traffic light target detection method based on i...

引用

7th International Conference on Electronic Information Technology and Computer Engineering (EITCE)

作者： Li, Shixin Wang, Kun Chen, Fankai Meng, Yue Tianjin Univ Technol & Educ Sch Elect Engn Tianjin Peoples R China China Telecom Fujian Branch Tianjin Peoples R China

ISBN: (纸本)9798400708305

Aiming at the real-time requirement of traffic light target detection in assisted driving, a traffic light target detection model based on improved yolov5 algorithm is established. The improved model is improved from two aspects of the original yolov5 model: C3 module and feature extraction network. The feature fusion mode of C3 module is changed from concat operation to add operation, and the Focus module in the feature extraction network is eliminated. The parameter configuration is optimized to avoid frequent slicing operations, and the parameters of the model are greatly reduced. The improved yolov5-lite model improves the processing speed while maintaining the detection accuracy, and is easier to deploy on the vehicle platform.

关键词： Object Detection Traffic Sign yolov5 algorithm Lightweight

来源：评论

学校读者我要写书评

暂无评论

Automated detection of soldering splashes using yolov5 algorithm 27

Automated detection of soldering splashes using YOLOv5 algor...

引用

27th International Conference on Applied Electronics (AE)

作者： Klco, Peter Koniar, Dusan Hargas, Libor Paskala, Marek Univ Zilina Dept Mechatron & Elect FEEIT Zilina Slovakia

ISBN: (数字)9781665494823

ISBN: (纸本)9781665494823

This paper deals with automated visual inspection of electronic boards in serial manufacturing of power electronics devices. Soldering splashes generated in the relevant phases of manufacturing can decrease the quality, parameters and lifetime of hybrid power semiconductor modules. Soldering splashes can occur in restricted area of electronic board and must be removed. Automated inspection is provided using neural network YOLO trained on image dataset of electronic boards acquired by authors in SEMIKRON Slovakia company. Implemented method will lead to higher reliability of manufacturing process.

关键词： hybrid power semiconductor soldering splash visual inspection convolutional neural network yolov5 algorithm

来源：评论

学校读者我要写书评

暂无评论

An Modified yolov5 algorithm to Improved Image Identification for Autonomous Driving

An Modified YOLOv5 Algorithm to Improved Image Identificatio...

引用

2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023

作者： Wang, Chun-Chieh Lu, Yi-Shun Lin, Wen-Piao Chang Gung University Department of Electrical Engineering Taoyuan Taiwan

ISBN: (纸本)9798350327595

In this paper we propose a solution to improve image identification on the early expressway engineering warning signs for autonomous driving. This scheme uses the modified lightweight yolov5 algorithm model to train feature categories. After manual labeling and data augmentation, the dataset is sent to the advance deep neural network for training. The practical experimental results show that the modified algorithm model can effectively identify engineering warning signs on the expressway. It allows drivers and construction units on the expressway to use the road more safely. © 2023 IEEE.

关键词： autonomous driving data augmentation deep neural network identification yolov5 algorithm

来源：评论

学校读者我要写书评

暂无评论

A Behavior Recognition Method for Elderly People Based on Background Cumulative Subtraction and yolov5 algorithm 24

A Behavior Recognition Method for Elderly People Based on Ba...

引用

3rd International Conference on Computer, Artificial Intelligence and Control Engineering, CAICE 2024

作者： Yin, Xuan Zhao, Bin Wu, Linshuo Wang, Shaokang Feng, Jun Xiong, Binyu Chen, Yijia School of Automation Wuhan University of Technology Wuhan China The University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9798400716942

To solve the problem of elderly people who are living alone and unable to receive timely assistance in the event of accidents and are prone to death, this paper proposed a behavior recognition method based on background cumulative subtraction method and yolov5 algorithm. Based on the filmed videos, the background cumulative subtraction method is proposed for motion object detection. The experimental results show that this method can effectively improve the detection of IoU and improve accuracy. Then, a behavior recognition model is trained by yolov5 algorithm. The model achieved a mAP of 0.983 at an IoU threshold of 0.5, and an F1Score of 0.96 at a confidence threshold of 0.443, indicating high model accuracy. Finally, this paper combines the background cumulative difference method and yolov5 algorithm to improve the speed of behavior recognition. The experimental results show that the proposed algorithm can reduce the detection time of each frame by 8.47% and improve real-time performance. © 2024 ACM.

关键词： Abnormal behavior detection Background Cumulative difference method Behavior recognition Elderly people yolov5 algorithm

来源：评论

学校读者我要写书评

暂无评论

Enhancing Target Detection and Recognition in Advanced Driver Assistance Systems Using Infrared Thermal Imaging and the yolov5 algorithm

引用

INTERNATIONAL JOURNAL OF HEAT AND TECHNOLOGY 2024年第5期42卷 1761-1768页

作者： Zhong, Mingxia Jiang, Bojun Zhejiang Business Coll Inst Elect Commerce Hangzhou 310059 Peoples R China Zhejiang Business Coll Sch Appl Engn & Technol Hangzhou 310059 Peoples R China

The potential of Advanced Driving Assistance Systems (ADAS) to enhance road safety is considerable;however, the reliability of ADAS in detecting and classifying road entities under varied environmental conditions remains a critical challenge. Conventional ADAS sensors often encounter limitations in adverse weather and low-visibility conditions, such as nighttime, rain, snow, and haze, reducing their capacity to detect vehicles and pedestrians effectively. To address these limitations, this study explores the integration of infrared thermal imaging technology into standard automotive sensor kits to enhance target detection capabilities. The yolov5 deep learning algorithm is applied to infrared thermal imaging data, aiming to improve the detection and classification of road targets, including pedestrians and motor vehicles, across diverse driving scenarios. Experimental results demonstrate that the proposed approach significantly enhances target detection, maintaining a balance between detection accuracy and real-time performance, particularly under challenging visibility conditions. These findings indicate that the integration of infrared thermal imaging with yolov5 in ADAS could reduce accident risks and improve road safety by providing more reliable scene analysis under adverse conditions.

关键词： Advanced Driving Assistance System(ADAS) Aautomotive sensor infrared thermal imaging target detection yolov5 algorithm deep learning low-visibility condition

来源：评论

学校读者我要写书评

暂无评论

4AC-yolov5: an improved algorithm for small target face detection

引用

EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING 2024年第1期2024卷 10页

作者： Jiang, Bin Jiang, Hongbin Zhang, Huanlong Zhang, Qiuwen Li, Zuhe Huang, Lixun Zhengzhou Univ Light Ind Sch Elect & Informat Zhengzhou 450001 Peoples R China Zhengzhou Univ Light Ind Coll Elect & Informat Engn Zhengzhou 450001 Peoples R China Zhengzhou Univ Light Ind Sch Comp Sci & Technol Zhengzhou 450001 Peoples R China

In real scenes, small target faces often encounter various conditions, such as intricate background, occlusion and scale change, which leads to the problem of omission or misdetection of face detection results. To solve this puzzle, an improved algorithm of small target face detection 4AC-yolov5 is proposed. First, the algorithm by introducing a new layer to detect faces at a much smaller size, through the fusion of more shallow information, enhance the network perception of small objects, the accuracy of small target detection is improved;second, to improve the neck structure, to add the adaptive feature fusion network AFPN to replace FPN + PAN, to prevent the large information gap between non-adjacent Level to some extent, and to fully retain and integrate different scale characteristic information;and finally, improve the C3 module and propose a new multiscale residual module C3_MultiRes. Improving the expressive power of the network by introducing a multibranched structure and gradually increasing resolution somewhat reduces the complexity of the model calculation. The experimental results show that the precision of the improved model reached 94.54%, 93.08% and 84.98% in easy, medium and hard levels of WiderFace data set, respectively, and the results of detection are better than the original network. 4AC-yolov5 can meet the requirements of small target face detection in complex environment.

关键词： Small target face Face detection yolov5 algorithm Multi-scale

来源：评论

学校读者我要写书评

暂无评论

Engineering Vehicle Recognition and Multi-Object Tracking System based on yolov5 and DeepSort algorithm 23

Engineering Vehicle Recognition and Multi-Object Tracking Sy...

引用

Proceedings of the 2023 5th International Conference on Video, Signal and Image Processing

作者： Yanbo Wu Xintao Liang Quan Cui Qingyan Wang Heilongjiang Government Affairs Big Data Center China School of Measurement-Control and Communication Engineering Harbin University of Science and Technology China School of Electronics and Information Engineering Harbin Institute of Technology China

ISBN: (纸本)9798400709272

This study explores traditional object detection algorithms and deep neural network-based engineering vehicle detection algorithms. We applied preprocessing algorithms such as image denoising, enhancement, and edge detection to process the images and constructed our own trainable dataset through labeling. Next, we used the yolov5 algorithm, which has high detection accuracy and real-time capability, for engineering vehicle detection. To address the issues of missed targets and predicted bounding box misalignment, we introduced the DeepSORT algorithm for target prediction and tracking. This algorithm utilizes Kalman filtering for estimation and updates and employs the Hungarian algorithm to associate data between consecutive frames, thereby achieving engineering vehicle tracking. To tackle the problem of frequent identity switching due to camera motion and non-uniform vehicle movements, we adopted the modified GIoU to calculate the intersection over union between trajectories and detected target bounding boxes, reducing identity jumps during the tracking process. Finally, we designed an engineering vehicle detection and tracking system and applied this algorithm to practical production.

关键词： DeepSORT algorithm yolov5 algorithm engineering vehicle detection multi-object tracking real-time detection

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：