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An implementation of the yolo algorithm for the recognition of flags of the International Code of Signals

Procedia Computer Science 2024年 246卷 3753-3760页

作者： Jakub Krajewski Ireneusz Czarnowski Gdynia Maritime University Morska 83 81-225 Gdynia Poland

The International Code of Signals (INTERCO) consists of codes and signals used to communicate messages regarding the safety of navigation. These codes are used by vessels, and the signals can be sent in various ways, including flaghoist, signal lamp, flag semaphore, or as a radio message. This paper focuses only on flag signals, proposing their detection and classification using deep learning algorithms. The paper also addresses a specific scenario, where the detection and classification of flag signals are necessary for verifying the current status, operations, and behaviours of vessels, in line with the intention conveyed by the flag signal. The system discussed is regarded as a component of a larger system for ensuring navigation safety and monitoring ship behaviours near critical infrastructure such as ports, offshore wind farms, or other offshore constructions. To tackle this challenge, a convolutional neural network (CNN) has been employed. The paper delves into the process of preparing the image dataset, including annotation and augmentation, and discusses the CNN learning process based on the yolo algorithm implementation, finally discussing the results of the computational experiment.

关键词： International Code of Signals image recognition convolutional neural network yolo algorithm

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Identification of Garbage in the River Based on The yolo algorithm

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INTERNATIONAL JOURNAL OF ELECTRONICS AND TELECOMMUNICATIONS 2021年第4期67卷 727-733页

作者： Suprapto, Bhakti Yudho Kelvin Kurniawan, Muhammad Arief Ardela, Muhammad Kevin Hikmarika, Hera Husin, Zainal Dwijayanti, Suci Univ Sriwijaya Fac Engn Dept Elect Engn Palembang Indonesia

This paper discusses the identification of garbage using the yolo algorithm. In the rivers, it is usually difficult to distinguish between garbage and plants, especially when it is done in real-time and at the time of too much light. Therefore, there is a need of an appropriate method. The HSV and SIFT methods were used as preliminary tests. The tests were quite successful even in close condition, however, there were still many problems faced in using this method since it is only based on pixel and shape readings. Meanwhile, yolo algorithm was able to identify garbage and water hyacinth even though they were closed to each other.

关键词： control identification HSV and sift method USV yolo algorithm

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Application of Improved yolo algorithm in the Design of FSAC Racing Real-time Target Detection System 21

Application of Improved Yolo algorithm in the Design of FSAC...

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Proceedings of the 5th International Conference on Advances in Image Processing

作者： Jie Sun Jianghan University China

ISBN: (纸本)9781450385183

In order to further improve the accuracy of FSAC racing target detection, this paper explores the modified method of yolo algorithm, puts it into the design of target detection system, and puts forward the application scheme of system design. The scheme uses k-means algorithm to design clustering processing scheme. By reducing the detection target specification, the control algorithm is developed in ROS system to realize high-precision target detection. The test results show that the target detection accuracy of the system is significantly improved compared with the original algorithm, and the processing efficiency is accelerated.

关键词： FSAC racing yolo algorithm Target detection

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Key Frame Extraction of Evidence Video Based on yolo algorithm 20

Key Frame Extraction of Evidence Video Based on YOLO Algorit...

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Proceedings of the 4th International Conference on Advances in Image Processing

作者： Jinming Min Jihong Zheng Gareth J.F. Jones China Shipbuilding Industry China Dublin City University Ireland

ISBN: (纸本)9781450388368

For the massive forensic video data, if we watch and intercept the key information manually, it will take a lot of time and delay the opportunity of obtaining useful evidence. For real-time processing the data with high accuracy, the computer automatically extracts the video frames with high-value content (including the image frames of specified types of aircraft, ships, and military uniforms in our experiments) through automatic video analysis. This method achieves the preliminary screening requirements for the key-frames, thus greatly reducing the time required for manually processing a large number of images. In this work, we propose a new method to utilize yolo algorithm to conduct key-frame extraction in processing the video data.

关键词： Key-frame Video summarization Object tracking yolo algorithm

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RESEARCH AND DESIGN OF INTELLIGENT PARKING MANAGEMENT SYSTEM BASED ON THE yolo algorithm

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第6期25卷 4940-4949页

作者： Tang, Mingjun Ge, Kunpeng Dai, Jun Guo, Linyang Shan, Dan Yangzhou Polytech Inst Sch Informat Engn Yangzhou Peoples R China Suqian Univ Coll Informat Engn Suqian Jiangsu Peoples R China

Given the difficulty in managing parking lots and inefficient utilization of parking spaces at university and college campuses, this paper designs an intelligent campus parking management system with functions such as license plate recognition (LPR), online parking space reservations, campus parking navigation, and mobile app payment through the parking space detection technology based on You Only Look Once (yolo) algorithm, Internet of Things (IoT) technology, and cloud platform technology. This system obtains information relating to license plates, parking spaces, etc. from sensing nodes with the IoT technology and transmits the collected information through NB-IoT technology to a cloud platform for storage and management to facilitate information exchange between hardware and software. This paper describes the architecture of the intelligent parking management system, parking space detection technology based on the yolov4 algorithm, and hardware and software design of the system. The paper proposes an effective solution to parking problems facing colleges and universities. The paper concludes that the intelligent campus parking management system based on the yolov4 algorithm can prevent the haphazard parking of vehicles and traffic congestion at open campuses and therefore has important value for application and popularization.

关键词： yolo algorithm IoT technology Cloud platform technology Parking management system

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Automatic Detection of Brain Tumor on MRI Images Using a yolo-Based algorithm 13

Automatic Detection of Brain Tumor on MRI Images Using a YOL...

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13th Iranian/3rd International Machine Vision and Image Processing Conference (MVIP)

作者： Rahimi, Masume Mostafavi, Mohammad Arabameri, Abazar Univ Zanjan Fac Elect & Comp Engn Zanjan Iran

ISBN: (纸本)9798350350494;9798350350500

This paper presents a study on the application of transfer learning and fine-tuning techniques to a deep learning model for the purpose of detecting three specific types of brain tumors from MRI images. The proposed approach utilizes the yolo algorithm for automatic diagnosis. Specifically, the yolov4-tiny model, which is a smaller version of the yolov4 algorithm, was trained and evaluated due to its improved performance. The dataset utilized in this research is obtained from the figshare data repository, which comprises of labeled MRI images. The division of the dataset resulted in 80% for training, 10% for validation, and 10% for testing purposes. Additionally, a pre-processing technique was devised to enhance the features in the MRI images. The outcomes of the implementation demonstrate that the yolov4-tiny model obtained a mean average precision (mAP) of 0.8074 for the raw data and 0.8324 for the processed data.

关键词： Object detection algorithm yolo algorithm bounding box transfer learning

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yolo-STOD: an industrial conveyor belt tear detection model based on yolov5 algorithm

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SCIENTIFIC REPORTS 2025年第1期15卷 1-21页

作者： Liu, Wei Tao, Qing Wang, Nini Xiao, Wendong Pan, Cen Xinjiang Univ Sch Mech Engn Urumqi 830000 Peoples R China Xinjiang Univ Coll Elect Engn Urumqi 830000 Peoples R China

Real-time detection of conveyor belt tearing is of great significance to ensure mining in the coal industry. The longitudinal tear damage problem of conveyor belts has the characteristics of multi-scale, abundant small targets, and complex interference sources. Therefore, in order to improve the performance of small-size tear damage detection algorithms under complex interference, a visual detection method yolo-STOD based on deep learning was proposed. Firstly, a multi-case conveyor belt tear dataset is developed for complex interference and small-size detection. Second, the detection method yolo-STOD is designed, which utilizes the BotNet attention mechanism to extract multi-dimensional tearing features, enhancing the model's feature extraction ability for small targets and enables the model to converge quickly under the conditions of few samples. Secondly, Shape_IOU is utilized to calculate the training loss, and the shape regression loss of the bounding box itself is considered to enhance the robustness of the model. The experimental results fully proved the effectiveness of the yolo-STOD detection method, which constantly surpasses the competing methods and achieves 91.2%, 91.9%, and 190.966 detection accuracy and detection speed in terms of recall, Map value, and FPS, respectively, which is able to satisfy the needs of industrial real-time detection and is expected to be used in the real-time detection of conveyor belt tearing in the industrial field.

关键词： BELT tearing yolo algorithm Real-time detection Deep learning Small object detection

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Using a yolo Deep Learning algorithm to Improve the Accuracy of 3D Object Detection by Autonomous Vehicles

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WORLD ELECTRIC VEHICLE JOURNAL 2025年第1期16卷 9-9页

作者： Murendeni, Ramavhale Mwanza, Alfred Obagbuwa, Ibidun Christiana Sol Plaatje Univ Fac Nat & Appl Sci Dept Comp Sci & Informat Technol ZA-8301 Kimberley South Africa

This study presents an adaptation of the yolov4 deep learning algorithm for 3D object detection, addressing a critical challenge in autonomous vehicle (AV) systems: accurate real-time perception of the surrounding environment in three dimensions. Traditional 2D detection methods, while efficient, fall short in providing the depth and spatial information necessary for safe navigation. This research modifies the yolov4 architecture to predict 3D bounding boxes, object depth, and orientation. Key contributions include introducing a multi-task loss function that optimizes 2D and 3D predictions and integrating sensor fusion techniques that combine RGB camera data with LIDAR point clouds for improved depth estimation. The adapted model, tested on real-world datasets, demonstrates a significant increase in 3D detection accuracy, achieving a mean average precision (mAP) of 85%, intersection over union (IoU) of 78%, and near real-time performance at 93-97% for detecting vehicles and 75-91% for detecting people. This approach balances high detection accuracy and real-time processing, making it highly suitable for AV applications. This study advances the field by showing how an efficient 2D detector can be extended to meet the complex demands of 3D object detection in real-world driving scenarios without sacrificing computational efficiency.

关键词： convolutional neural networks deep learning machine learning object detection yolo algorithm

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

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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

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

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

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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

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