Log in

Relevant bibliographies by topics / You only look once version 5 / Journal articles

To see the other types of publications on this topic, follow the link: You only look once version 5.

Journal articles on the topic 'You only look once version 5'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'You only look once version 5.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Lee, Tae-Young, Seung Bae Jeon, and Myeong-Hun Jeong. "Marine Debris Detection Using Optimized You Only Look Once Version 5." Sensors and Materials 35, no. 9 (2023): 3441. http://dx.doi.org/10.18494/sam4477.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Meng, Mingzhu, Ming Zhang, Dong Shen, Guangyuan He, and Yi Guo. "Detection and Classification of Breast Lesions with You Only Look Once Version 5." Future Oncology 18, no. 39 (2022): 4361–70. http://dx.doi.org/10.2217/fon-2022-0593.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Al-Haimi, Hamzah Abdulmalek, Zamani Md Sani, Tarmizi Ahmad Izzudin, Hadhrami Abdul Ghani, Azizul Azizan, and Karim Samsul Ariffin Abdul. "Traffic light counter detection comparison using you only look oncev3 and you only look oncev5 for version 3 and 5." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (2023): 1585–92. https://doi.org/10.11591/ijai.v12.i4.pp1585-1592.

Full text

Abstract:

This project aims to develop a vision system that can detect traffic light counter and to recognise the numbers shown on it. The system used you only look once version 3 (YOLOv3) algorithm because of its robust performance and reliability and able to be implemented in Nvidia Jetson nano kit. A total of 2204 images consisting of numbers from 0-9 green and 0-9 red. Another 80% (1764) from the images are used for training and 20% (440) are used for testing. The results obtained from the training demonstrated Total precision=89%, Recall=99.2%, F1 score=70%, intersection over union (IoU)=70.49%, me

APA, Harvard, Vancouver, ISO, and other styles

4

Al-Haimi, Hamzah Abdulmalek, Zamani Md Sani, Tarmizi Ahmad Izzudin, Hadhrami Abdul Ghani, Azizul Azizan, and Samsul Ariffin Abdul Karim. "Traffic light counter detection comparison using you only look oncev3 and you only look oncev5 for version 3 and 5." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (2023): 1585. http://dx.doi.org/10.11591/ijai.v12.i4.pp1585-1592.

Full text

Abstract:

<p>This project aims to develop a vision system that can detect traffic light<br />counter and to recognise the numbers shown on it. The system used you only<br />look once version 3 (YOLOv3) algorithm because of its robust performance<br />and reliability and able to be implemented in Nvidia Jetson nano kit. A total<br />of 2204 images consisting of numbers from 0-9 green and 0-9 red. Another<br />80% (1764) from the images are used for training and 20% (440) are used for<br />testing. The results obtained from the training demonstrated Total<br /&

APA, Harvard, Vancouver, ISO, and other styles

5

Aicha, Khalfaoui, Badri Abdelmajid, and El Mourabit Ilham. "A lightweight you only look once for real-time dangerous weapons detection." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 1838–44. https://doi.org/10.11591/ijai.v13.i2.pp1838-1844.

Full text

Abstract:

Deep neural networks are currently employed to detect weapons, and although these techniques provide a high level of accuracy, it still suffers from large weight parameters and a slow inference speed. When considering real-world applications like weapon detection, these methods are frequently unsuitable for deployment on embedded devices due to their large number of parameters and poor efficiency. The most recent object detection technique, which falls under the YOLOv5 (You Only Look Once version 5) family, is commonly used for detecting weapons. However, it faces some difficulties such as hig

APA, Harvard, Vancouver, ISO, and other styles

6

Liang, Yu, Sai Li, Guanting Ye, Qing Jiang, Qiang Jin, and Yifei Mao. "Autonomous surface crack identification for concrete structures based on the you only look once version 5 algorithm." Engineering Applications of Artificial Intelligence 133 (July 2024): 108479. http://dx.doi.org/10.1016/j.engappai.2024.108479.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Li, Yan. "Virtual sports interactive system design integrating ghost net network and improved YOLOv5 algorithm." International Journal for Simulation and Multidisciplinary Design Optimization 15 (2024): 19. http://dx.doi.org/10.1051/smdo/2024016.

Full text

Abstract:

With the development of virtual reality, the human–computer interaction through virtual sports is gradually maturing, and users are gradually looking to interact with the two-dimensional world. The research on this type of algorithm has gained attention. However, due to the delay of the old transmission technology in the transmission of pictures, which is higher than the reaction time of human brain, the pictures are inconsistent and illogical, and the user interaction experience is poor. To solve it, this research realizes the fusion of ghost network and You Only Look Once version 5, and the

APA, Harvard, Vancouver, ISO, and other styles

8

Wan, Xueqiang, Jiong Yu, Haotian Tan, and Junjie Wang. "LAG: Layered Objects to Generate Better Anchors for Object Detection in Aerial Images." Sensors 22, no. 10 (2022): 3891. http://dx.doi.org/10.3390/s22103891.

Full text

Abstract:

You Only Look Once (YOLO) series detectors are suitable for aerial image object detection because of their excellent real-time ability and performance. Their high performance depends heavily on the anchor generated by clustering the training set. However, the effectiveness of the general Anchor Generation algorithm is limited by the unique data distribution of the aerial image dataset. The divergence in the distribution of the number of objects with different sizes can cause the anchors to overfit some objects or be assigned to suboptimal layers because anchors of each layer are generated unif

APA, Harvard, Vancouver, ISO, and other styles

9

Wang, Haiyan, Zhan Shi, Guiyuan Gao, Chuang Li, Jian Zhao, and Zhiwei Xu. "Robot Operating Systems–You Only Look Once Version 5–Fleet Efficient Multi-Scale Attention: An Improved You Only Look Once Version 5-Lite Object Detection Algorithm Based on Efficient Multi-Scale Attention and Bounding Box Regression Combined with Robot Operating Systems." Applied Sciences 14, no. 17 (2024): 7591. http://dx.doi.org/10.3390/app14177591.

Full text

Abstract:

This paper primarily investigates enhanced object detection techniques for indoor service mobile robots. Robot operating systems (ROS) supply rich sensor data, which boost the models’ ability to generalize. However, the model’s performance might be hindered by constraints in the processing power, memory capacity, and communication capabilities of robotic devices. To address these issues, this paper proposes an improved you only look once version 5 (YOLOv5)-Lite object detection algorithm based on efficient multi-scale attention and bounding box regression combined with ROS. The algorithm incor

APA, Harvard, Vancouver, ISO, and other styles

10

Chen, Yen-Chiu, Kun-Ming Yu, Tzu-Hsiang Kao, and Hao-Lun Hsieh. "Deep learning based real-time tourist spots detection and recognition mechanism." Science Progress 104, no. 3_suppl (2021): 003685042110442. http://dx.doi.org/10.1177/00368504211044228.

Full text

Abstract:

More and more information on tourist spots is being represented as pictures rather than text. Consequently, tourists who are interested in a specific attraction shown in pictures may have no idea how to perform a text search to get more information about the interesting tourist spots. In the view of this problem and to enhance the competitiveness of the tourism market, this research proposes an innovative tourist spot identification mechanism, which is based on deep learning-based object detection technology, for real-time detection and identification of tourist spots by taking pictures on loc

APA, Harvard, Vancouver, ISO, and other styles

11

Aydin, Burchan, and Subroto Singha. "Drone Detection Using YOLOv5." Eng 4, no. 1 (2023): 416–33. http://dx.doi.org/10.3390/eng4010025.

Full text

Abstract:

The rapidly increasing number of drones in the national airspace, including those for recreational and commercial applications, has raised concerns regarding misuse. Autonomous drone detection systems offer a probable solution to overcoming the issue of potential drone misuse, such as drug smuggling, violating people’s privacy, etc. Detecting drones can be difficult, due to similar objects in the sky, such as airplanes and birds. In addition, automated drone detection systems need to be trained with ample amounts of data to provide high accuracy. Real-time detection is also necessary, but this

APA, Harvard, Vancouver, ISO, and other styles

12

Liu, Yuhang, Yuqiao Zheng, Tai Wei, and Yabing Li. "Lightweight algorithm based on you only look once version 5 for multiple class defect detection on wind turbine blade surfaces." Engineering Applications of Artificial Intelligence 138 (December 2024): 109422. http://dx.doi.org/10.1016/j.engappai.2024.109422.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Fadhlan Hafizhelmi Kamaru Zaman, Nooritawati Md Tahir, Yusnani Mohd Yusoff, Norashikin M. Thamrin, and Ahmad Hafizam Hasmi. "Human Detection from Drone using You Only Look Once (YOLOv5) for Search and Rescue Operation." Journal of Advanced Research in Applied Sciences and Engineering Technology 30, no. 3 (2023): 222–35. http://dx.doi.org/10.37934/araset.30.3.222235.

Full text

Abstract:

Drones are unmanned aerial vehicles that can be remotely operated to perform a variety of tasks. They have been used in search and rescue operations since the early 2000s and have proven to be invaluable tools for quickly locating missing persons in difficult terrain and environment. In certain cases, automated human detection on drone camera feed can help the responder to locate the victims more effectively. In this work, we propose the use of a deep learning method called You Only Look Once version 5, or YOLOv5. The YOLOv5 model is trained using data collected during a simulation of search a

APA, Harvard, Vancouver, ISO, and other styles

14

Kılıçkaya, Fatma Nur, Murat Taşyürek, and Celal Öztürk. "Performance evaluation of YOLOv5 and YOLOv8 models in car detection." Imaging and Radiation Research 6, no. 2 (2024): 5757. http://dx.doi.org/10.24294/irr.v6i2.5757.

Full text

Abstract:

Vehicle detection stands out as a rapidly developing technology today and is further strengthened by deep learning algorithms. This technology is critical in traffic management, automated driving systems, security, urban planning, environmental impacts, transportation, and emergency response applications. Vehicle detection, which is used in many application areas such as monitoring traffic flow, assessing density, increasing security, and vehicle detection in automatic driving systems, makes an effective contribution to a wide range of areas, from urban planning to security measures. Moreover,

APA, Harvard, Vancouver, ISO, and other styles

15

Hesham, Mai, Ghada Kareem, and Marwa Hadhoud. "Enhanced real-time glaucoma diagnosis: dual deep learning approach." Bulletin of Electrical Engineering and Informatics 14, no. 3 (2025): 1846–57. https://doi.org/10.11591/eei.v14i3.8495.

Full text

Abstract:

Effective management of glaucoma is essential for preventing irreversible vision loss. This study introduces a novel deep learning-based network designed to enhance performance while minimizing computational complexity. The system comprises two models: the first is a hybrid model combining a customized U-Net architecture integrated with you only look at coefficients (YOLACT) is utilized to achieve accurate segmentation of the optic disc (OD) and optic cup (OC), providing detailed diagnostic insights for ophthalmologists. The second model employs you only look once version 5 (YOLOv5) for real-t

APA, Harvard, Vancouver, ISO, and other styles

16

Ma, Yihai, Guowu Yuan, Kun Yue, and Hao Zhou. "CJS-YOLOv5n: A high-performance detection model for cigarette appearance defects." Mathematical Biosciences and Engineering 20, no. 10 (2023): 17886–904. http://dx.doi.org/10.3934/mbe.2023795.

Full text

Abstract:

<abstract> <p>In tobacco production, cigarettes with appearance defects are inevitable and dramatically impact the quality of tobacco products. Currently, available methods do not balance the tension between detection accuracy and speed. To achieve accurate detection on a cigarette production line with the rate of 200 cigarettes per second, we propose a defect detection model for cigarette appearance based on YOLOv5n (You Only Look Once Version 5 Nano), called CJS-YOLOv5n (YOLOv5n with C2F (Cross Stage Partial (CSP) Bottleneck with 2 convolutions-fast), Jump Concat, and SCYLLA-IoU

APA, Harvard, Vancouver, ISO, and other styles

17

Yuan, Kai, Qian Wang, Yalong Mi, Yangfan Luo, and Zuoxi Zhao. "Improved Feature Fusion in YOLOv5 for Accurate Detection and Counting of Chinese Flowering Cabbage (Brassica campestris L. ssp. chinensis var. utilis Tsen et Lee) Buds." Agronomy 14, no. 1 (2023): 42. http://dx.doi.org/10.3390/agronomy14010042.

Full text

Abstract:

Chinese flowering cabbage (Brassica campestris L. ssp. chinensis var. utilis Tsen et Lee) is an important leaf vegetable originating from southern China. Its planting area is expanding year by year. Accurately judging its maturity and determining the appropriate harvest time are crucial for production. The open state of Chinese flowering cabbage buds serves as a crucial maturity indicator. To address the challenge of accurately identifying Chinese flowering cabbage buds, we introduced improvements to the feature fusion approach of the YOLOv5 (You Only Look Once version 5) algorithm, resulting

APA, Harvard, Vancouver, ISO, and other styles

18

Nguyen, Quoc Toan. "Detrimental Starfish Detection on Embedded System: A Case Study of YOLOv5 Deep Learning Algorithm and TensorFlow Lite framework." Journal of Computer Sciences Institute 23 (June 30, 2022): 105–11. http://dx.doi.org/10.35784/jcsi.2896.

Full text

Abstract:

There is a great range of spectacular coral reefs in the ocean world. Unfortunately, they are in jeopardy, due to an overabundance of one specific starfish called the coral-eating crown-of-thorns starfish (or COTS). This article provides research to deliver innovation in COTS control. Using a deep learning model based on the You Only Look Once version 5 (YOLOv5) deep learning algorithm on an embedded device for COTS detection. It aids professionals in optimizing their time, resources and enhancing efficiency for the preservation of coral reefs all around the world. As a result, the performance

APA, Harvard, Vancouver, ISO, and other styles

19

Shili, Mohamed, Osama Sohaib, and Salah Hammedi. "You Only Look Once Version 5 and Deep Simple Online and Real-Time Tracking Algorithms for Real-Time Customer Behavior Tracking and Retail Optimization." Algorithms 17, no. 11 (2024): 525. http://dx.doi.org/10.3390/a17110525.

Full text

Abstract:

The speedy progress of computer vision and machine learning engineering has inaugurated novel means for improving the purchasing experiment in brick-and-mortar stores. This paper examines the utilization of YOLOv (You Only Look Once) and DeepSORT (Deep Simple Online and Real-Time Tracking) algorithms for the real-time detection and analysis of the purchasing penchant in brick-and-mortar market surroundings. By leveraging these algorithms, stores can track customer behavior, identify popular products, and monitor high-traffic areas, enabling businesses to adapt quickly to customer preferences a

APA, Harvard, Vancouver, ISO, and other styles

20

Yasiri, Jamilatur Rizqil, Rastri Prathivi, and Susanto Susanto. "Detection of Plastic Bottle Waste Using YOLO Version 5 Algorithm." sinkron 9, no. 1 (2025): 20–30. https://doi.org/10.33395/sinkron.v9i1.14242.

Full text

Abstract:

Plastic bottle waste management has become one of the most pressing environmental issues, especially in countries with high plastic usage rates, such as Indonesia. This research uses the YOLOv5 (You Only Look Once version 5) algorithm to detect plastic bottle waste automatically. The YOLOv5 algorithm was chosen because it has efficient detection performance and high accuracy in small object recognition. The dataset consists of 500 images of plastic bottles obtained through cameras and internet sources. The data is processed through several stages: annotation (bounding box and labeling using Ro

APA, Harvard, Vancouver, ISO, and other styles

21

Darmadi and Haidar Nur Doni. "Traffic Counting using YOLO Version-5 (A case study of Jakarta-Cikampek Toll Road)." IOP Conference Series: Earth and Environmental Science 1321, no. 1 (2024): 012015. http://dx.doi.org/10.1088/1755-1315/1321/1/012015.

Full text

Abstract:

Abstract The Jakarta-Cikampek toll road is the main access to the Tanjung Priok port, which is connected directly via the Cilincing-Tanjung Priuk Port toll road as a development of the North Jakarta reclamation coastal area. YOLO (You Only Look Once) is a common object detection model that offers faster and more accurate results.. The purpose of this article is to use advancements in information technology to automate the process of manually recording traffic counts on the highway. The method utilized in this study was to record a video of traffic movements with a smartphone camera and save it

APA, Harvard, Vancouver, ISO, and other styles

22

Thombre, Sakshi. "Object Detection Using YOLOv5: A Deep Learning Approach." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41361.

Full text

Abstract:

Object detection is a fundamental task in computer vision that involves identifying and localizing objects within images or video frames. This research focuses on implementing and evaluating the YOLOv5 (You Only Look Once version 5) model for real-time object detection. YOLOv5 is known for its efficiency, accuracy, and speed, making it a preferred choice for various applications such as autonomous driving, surveillance, and medical imaging. In this study, we explore the architecture, training process, and performance evaluation of YOLOv5 on benchmark datasets. The results demonstrate that YOLO

APA, Harvard, Vancouver, ISO, and other styles

23

Asaju, Christine Bukola, Pius Adewale Owolawi, Chuling Tu, and Etienne Van Wyk. "Cloud-Based License Plate Recognition: A Comparative Approach Using You Only Look Once Versions 5, 7, 8, and 9 Object Detection." Information 16, no. 1 (2025): 57. https://doi.org/10.3390/info16010057.

Full text

Abstract:

Cloud-based license plate recognition (LPR) systems have emerged as essential tools in modern traffic management and security applications. Determining the best approach remains paramount in the field of computer vision. This study presents a comparative analysis of various versions of the YOLO (You Only Look Once) object detection models, namely, YOLO 5, 7, 8, and 9, applied to LPR tasks in a cloud computing environment. Using live video, we performed experiments on YOLOv5, YOLOv7, YOLOv8, and YOLOv9 models to detect number plates in real time. According to the results, YOLOv8 is reported the

APA, Harvard, Vancouver, ISO, and other styles

24

Sulton, Ahmad, Fauzul Arya Ramadhan, Duman Care Khrisne, Made Sudarma, and I. Wayan Shandyasa. "PENERAPAN YOLOV5 DAN SORT DALAM DETEKSI KENDARAAN PADA PERSIMPANGAN BERSINYAL UNTUK PENYESUAIAN WAKTU LAMPU LALU LINTAS." Jurnal SPEKTRUM 11, no. 2 (2024): 113. http://dx.doi.org/10.24843/spektrum.2024.v11.i02.p12.

Full text

Abstract:

The problems with traffic in many big cities is congestion. Detection and tracking of vehicles in traffic light queues is an important aspect of an efficient urban transport system. This research introduces a combined model that combines the YOLO (You Only Look Once) version 5 algorithm or commonly called YOLOv5, a high-speed object detection approach, with the SORT (Simple Online and Realtime Tracking) algorithm to efficiently detect and track vehicles. The method was tested using a dataset of real traffic recordings and the results showed excellent performance in detecting and tracking vehic

APA, Harvard, Vancouver, ISO, and other styles

25

Kutlimuratov, Alpamis, Jamshid Khamzaev, Temur Kuchkorov, Muhammad Shahid Anwar, and Ahyoung Choi. "Applying Enhanced Real-Time Monitoring and Counting Method for Effective Traffic Management in Tashkent." Sensors 23, no. 11 (2023): 5007. http://dx.doi.org/10.3390/s23115007.

Full text

Abstract:

This study describes an applied and enhanced real-time vehicle-counting system that is an integral part of intelligent transportation systems. The primary objective of this study was to develop an accurate and reliable real-time system for vehicle counting to mitigate traffic congestion in a designated area. The proposed system can identify and track objects inside the region of interest and count detected vehicles. To enhance the accuracy of the system, we used the You Only Look Once version 5 (YOLOv5) model for vehicle identification owing to its high performance and short computing time. Ve

APA, Harvard, Vancouver, ISO, and other styles

26

H, Sivalingan. "PEDESTRIAN DETECTION IN VIDEO SURVEILLANCE USING YOLO V5 WITH LIGHT PERCEPTION FUSION." ICTACT Journal on Soft Computing 14, no. 4 (2024): 3347–53. http://dx.doi.org/10.21917/ijsc.2024.0470.

Full text

Abstract:

This research presents an innovative approach to pedestrian detection in video surveillance, leveraging the power of YOLOv5 (You Only Look Once version 5) combined with light perception fusion-based feature extraction. The proposed methodology aims to enhance the accuracy and efficiency of pedestrian detection systems in varying lighting conditions. YOLOv5, known for its real-time object detection capabilities, is integrated with a novel feature extraction technique that fuses information from multiple light perception sensors. This fusion strategy allows the model to adapt and perform robustl

APA, Harvard, Vancouver, ISO, and other styles

27

Jimiria, Pratama Siti Nurmaini Muhammad Fachrurrozi. "Deteksi Struktur Jantung pada anak menggunakan CNN Arsitektur YOLO versi 5." JUPITER 16, no. 2 (2024): 635–46. https://doi.org/10.5281/zenodo.13762983.

Full text

Abstract:

<em> A major challenge in the medical field is detecting heart structures in children, which requires a high level of time and accuracy. To address this issue, the You Only Look Once version 5 (YOLO v5) method is employed to identify children's heart structures using a convolutional neural network (CNN). YOLO v5s, YOLO v5n, and YOLO v5x are three versions tested to identify children's heart structures. Standard evaluation metrics such as precision, recall, F1 score, mean average precision, and IoU threshold 0.5 (mAP_0.5) are used to assess the model's performance. Experimental results ind

APA, Harvard, Vancouver, ISO, and other styles

28

Hajare, Gayatri, Utkarsh Kharche, Pritam Mahajan, and Apurva Shinde. "Automatic Number Plate Recognition System for Indian Number Plates using Machine Learning Techniques." ITM Web of Conferences 44 (2022): 03044. http://dx.doi.org/10.1051/itmconf/20224403044.

Full text

Abstract:

India being a country where the population is above 1.3 billion where each person has at least one car of his/her use. Considering this, the number of cars driven on the roads of India must be greater than the population of the people in the country. India being a diverse country, diversity is not only seen in the language of the number plates but also in size, spacing between the letters on the number plate and the font of the number plate. Diversity differs from state to state. Even though most of the people are using English Number plates, there is no certain law as to how a number plate sh

APA, Harvard, Vancouver, ISO, and other styles

29

Alkandary, Khadijah, Ahmet Serhat Yildiz, and Hongying Meng. "A Comparative Study of YOLO Series (v3–v10) with DeepSORT and StrongSORT: A Real-Time Tracking Performance Study." Electronics 14, no. 5 (2025): 876. https://doi.org/10.3390/electronics14050876.

Full text

Abstract:

Many previous studies have explored the integration of a specific You Only Look Once (YOLO) model with real-time trackers like Deep Simple Online and Realtime Tracker (DeepSORT) and Strong Simple Online and Realtime Tracker (StrongSORT). However, few have conducted a comprehensive and in-depth analysis of integrating the family of YOLO models with these real-time trackers to study the performance of the resulting pipeline and draw critical conclusions. This work aims to fill this gap, with the primary objective of investigating the effectiveness of integrating the YOLO series, in light-sized v

APA, Harvard, Vancouver, ISO, and other styles

30

Sun, Lijuan, Guangrui Hu, Chao Chen, et al. "Lightweight Apple Detection in Complex Orchards Using YOLOV5-PRE." Horticulturae 8, no. 12 (2022): 1169. http://dx.doi.org/10.3390/horticulturae8121169.

Full text

Abstract:

The detection of apple yield in complex orchards plays an important role in smart agriculture. Due to the large number of fruit trees in the orchard, improving the speed of apple detection has become one of the challenges of apple yield detection. Additional challenges in the detection of apples in complex orchard environments are vision obstruction by leaves, branches and other fruit, and uneven illumination. The YOLOv5 (You Only Look Once version 5) network structure has thus far been increasingly utilized for fruit recognition, but its detection accuracy and real-time detection speed can be

APA, Harvard, Vancouver, ISO, and other styles

31

Aloufi, Nasser, Abdulaziz Alnori, Vijey Thayananthan, and Abdullah Basuhail. "Object Detection Performance Evaluation for Autonomous Vehicles in Sandy Weather Environments." Applied Sciences 13, no. 18 (2023): 10249. http://dx.doi.org/10.3390/app131810249.

Full text

Abstract:

In order to reach the highest level of automation, autonomous vehicles (AVs) are required to be aware of surrounding objects and detect them even in adverse weather. Detecting objects is very challenging in sandy weather due to characteristics of the environment, such as low visibility, occlusion, and changes in lighting. In this paper, we considered the You Only Look Once (YOLO) version 5 and version 7 architectures to evaluate the performance of different activation functions in sandy weather. In our experiments, we targeted three activation functions: Sigmoid Linear Unit (SiLU), Rectified L

APA, Harvard, Vancouver, ISO, and other styles

32

Aloufi, Nasser, Abdulaziz Alnori, and Abdullah Basuhail. "Enhancing Autonomous Vehicle Perception in Adverse Weather: A Multi Objectives Model for Integrated Weather Classification and Object Detection." Electronics 13, no. 15 (2024): 3063. http://dx.doi.org/10.3390/electronics13153063.

Full text

Abstract:

Robust object detection and weather classification are essential for the safe operation of autonomous vehicles (AVs) in adverse weather conditions. While existing research often treats these tasks separately, this paper proposes a novel multi objectives model that treats weather classification and object detection as a single problem using only the AV camera sensing system. Our model offers enhanced efficiency and potential performance gains by integrating image quality assessment, Super-Resolution Generative Adversarial Network (SRGAN), and a modified version of You Only Look Once (YOLO) vers

APA, Harvard, Vancouver, ISO, and other styles

33

Duan, Hongchao, Jun Wang, Yuan Zhang, et al. "Shrimp Larvae Counting Based on Improved YOLOv5 Model with Regional Segmentation." Sensors 24, no. 19 (2024): 6328. http://dx.doi.org/10.3390/s24196328.

Full text

Abstract:

Counting shrimp larvae is an essential part of shrimp farming. Due to their tiny size and high density, this task is exceedingly difficult. Thus, we introduce an algorithm for counting densely packed shrimp larvae utilizing an enhanced You Only Look Once version 5 (YOLOv5) model through a regional segmentation approach. First, the C2f and convolutional block attention modules are used to improve the capabilities of YOLOv5 in recognizing the small shrimp. Moreover, employing a regional segmentation technique can decrease the receptive field area, thereby enhancing the shrimp counter’s detection

APA, Harvard, Vancouver, ISO, and other styles

34

Dzil, Fadhli, Agus Khumaidi, Mohammad Basuki Rahmat, Joko Endrasmono4, Mat Syai’in, and Dimas Pristovani Riananda. "Deteksi Objek di Lapangan pada Robot Sepakbola Beroda Menggunakan Metode YOLOV5." Jurnal Elektronika dan Otomasi Industri 11, no. 2 (2024): 604–11. http://dx.doi.org/10.33795/elkolind.v11i2.5235.

Full text

Abstract:

Penelitian ini bertujuan untuk mendeteksi objek seperti bola, robot, dan gawang pada robot sepak bola beroda dengan menggunakan metode You Only Look Once Version 5 (YOLOv5). Metode ini dipilih karena memiliki kemampuan deteksi yang cepat dan akurasi yang lebih tinggi dibandingkan versi YOLO sebelumnya. Dataset yang digunakan mencakup 4555 gambar yang terbagi menjadi data pelatihan dan validasi. Pelatihan model YOLOv5 dilakukan dengan parameter-parameter tertentu seperti ukuran gambar 416x416 piksel, ukuran batch 16, jumlah epoch 1000, dan parameter lainnya. Hasil pelatihan menunjukkan presisi

APA, Harvard, Vancouver, ISO, and other styles

35

Sun, Zhenhui, Ying Xu, Dongchuan Wang, Qingyan Meng, and Yunxiao Sun. "Using Improved YOLOv5 and SegFormer to Extract Tailings Ponds from Multi-Source Data." Photogrammetric Engineering & Remote Sensing 90, no. 4 (2024): 223–31. http://dx.doi.org/10.14358/pers.23-00066r2.

Full text

Abstract:

This paper proposes a framework that combines the improved "You Only Look Once" version 5 (YOLOv5) and SegFormer to extract tailings ponds from multi-source data. Points of interest (POIs) are crawled to capture potential tailings pond regions. Jeffries–Matusita distance is used to evaluate the optimal band combination. The improved YOLOv5 replaces the backbone with the PoolFormer to form a PoolFormer backbone. The neck introduces the CARAFE operator to form a CARAFE feature pyramid network neck (CRF-FPN). The head is substituted with an efficiency decoupled head. POIs and classification data

APA, Harvard, Vancouver, ISO, and other styles

36

Hong Quang, Nguyen, Hanna Lee, Ha Thi Thu Hue, Sumin Park, and Gihong Kim. "Lake detection and semantic segmentation using a deep learning model and Kompsat-5 images." IOP Conference Series: Earth and Environmental Science 1501, no. 1 (2025): 012015. https://doi.org/10.1088/1755-1315/1501/1/012015.

Full text

Abstract:

Abstract Surface water detection and extraction from remote sensing data is a favorable study topic due to the critical importance of this indispensable resource. For water resource management implementation, lake and reservoir detection and segmentation can supply valuable information. Korea Aerospace Research Institute (KARI) has developed and been operating the Kompsat-5 satellite acquiring high-resolution Synthetic-aperture radar (SAR) images. The Kompsat-5 sensor transmits a short radar wavelength of band-x (3.2 cm) obtaining more details of observed objects. However, that also results in

APA, Harvard, Vancouver, ISO, and other styles

37

Febrina Silalahi, Anggi Vivi, Agustina Setianing Budi, Januaray Satya Ega, et al. "APLIKASI PENERJEMAH BAHASA ISYARAT BISINDO MENGGUNAKAN METODE YOLOv5 BERBASIS MOBILE." Jurnal SPEKTRUM 11, no. 3 (2024): 13. http://dx.doi.org/10.24843/spektrum.2024.v11.i03.p2.

Full text

Abstract:

This research aims to develop a mobile application that is able to recognize and translate BISINDO in real-time using the YOLOv5 method. YOLOv5 (You Only Look Once version 5) is an efficient and fast object detection algorithm, making it suitable for implementation on mobile devices. In developing this application, we collected and labeled a typical BISINDO dataset, then trained the YOLOv5 model to detect and recognize these characteristics. The test results show that the implemented model has high accuracy in recognizing various BISINDO characteristics. Apart from that, this application is al

APA, Harvard, Vancouver, ISO, and other styles

38

Alateeq, Muneerah M., Fathimathul Rajeena P.P., and Mona A. S. Ali. "Construction Site Hazards Identification Using Deep Learning and Computer Vision." Sustainability 15, no. 3 (2023): 2358. http://dx.doi.org/10.3390/su15032358.

Full text

Abstract:

Workers on construction sites face numerous health and safety risks. Authorities have made numerous attempts to enhance safety management; yet incidents continue to occur, impacting both worker health and the project’s forward momentum. To that end, developing strategies to improve construction site safety management is crucial. The goal of this project is to employ computer vision and deep learning methods to create a model that can recognize construction workers, their PPE and the surrounding heavy equipment from CCTV footage. Then, the hazards can be discovered and identified based on an an

APA, Harvard, Vancouver, ISO, and other styles

39

Zhang, Long, Jiaming Li, and Fuquan Zhang. "An Efficient Forest Fire Target Detection Model Based on Improved YOLOv5." Fire 6, no. 8 (2023): 291. http://dx.doi.org/10.3390/fire6080291.

Full text

Abstract:

To tackle the problem of missed detections in long-range detection scenarios caused by the small size of forest fire targets, initiatives have been undertaken to enhance the feature extraction and detection precision of models designed for forest fire imagery. In this study, two algorithms, DenseM-YOLOv5 and SimAM-YOLOv5, were proposed by modifying the backbone network of You Only Look Once version 5 (YOLOv5). From the perspective of lightweight models, compared to YOLOv5, SimAM-YOLOv5 reduced the parameter size by 28.57%. Additionally, although SimAM-YOLOv5 showed a slight decrease in recall

APA, Harvard, Vancouver, ISO, and other styles

40

Gan, Ying Huey, Shih Yin Ooi, Ying Han Pang, Yi Hong Tay, and Quan Fong Yeo. "Facial Skin Analysis in Malaysians using YOLOv5: A Deep Learning Perspective." Journal of Informatics and Web Engineering 3, no. 2 (2024): 1–18. http://dx.doi.org/10.33093/jiwe.2023.3.2.1.

Full text

Abstract:

Nowadays, people are more concerned about their skin conditions and are more willing to spend money and time on facial care routines. The beauty sector market is increasing, and more skin type readers are being created to help people determine their skin type. While various skin type readers are in the market, each is invented and tested abroad. Those skin type readers in the beauty market are not applied well on Malaysian skin. Therefore, this paper proposes a facial skin analysis system tailored primarily for Malaysian skin. This paper integrated object detection and deep learning algorithms

APA, Harvard, Vancouver, ISO, and other styles

41

Qiu, Zhengjun, Nan Zhao, Lei Zhou, et al. "Vision-Based Moving Obstacle Detection and Tracking in Paddy Field Using Improved Yolov3 and Deep SORT." Sensors 20, no. 15 (2020): 4082. http://dx.doi.org/10.3390/s20154082.

Full text

Abstract:

Using intelligent agricultural machines in paddy fields has received great attention. An obstacle avoidance system is required with the development of agricultural machines. In order to make the machines more intelligent, detecting and tracking obstacles, especially the moving obstacles in paddy fields, is the basis of obstacle avoidance. To achieve this goal, a red, green and blue (RGB) camera and a computer were used to build a machine vision system, mounted on a transplanter. A method that combined the improved You Only Look Once version 3 (Yolov3) and deep Simple Online and Realtime Tracki

APA, Harvard, Vancouver, ISO, and other styles

42

Hao, Shengnan, Erjian Gao, Zhanlin Ji, and Ivan Ganchev. "BCS_YOLO: Research on Corn Leaf Disease and Pest Detection Based on YOLOv11n." Applied Sciences 15, no. 15 (2025): 8231. https://doi.org/10.3390/app15158231.

Full text

Abstract:

Frequent corn leaf diseases and pests pose serious threats to agricultural production. Traditional manual detection methods suffer from significant limitations in both performance and efficiency. To address this, the present paper proposes a novel biotic condition screening (BCS) model for the detection of corn leaf diseases and pests, called BCS_YOLO, based on the You Only Look Once version 11n (YOLOv11n). The proposed model enables accurate detection and classification of various corn leaf pathologies and pest infestations under challenging agricultural field conditions. It achieves this tha

APA, Harvard, Vancouver, ISO, and other styles

43

Hu, Meijia, Yantao Wei, Mengsiying Li, et al. "Bimodal Learning Engagement Recognition from Videos in the Classroom." Sensors 22, no. 16 (2022): 5932. http://dx.doi.org/10.3390/s22165932.

Full text

Abstract:

Engagement plays an essential role in the learning process. Recognition of learning engagement in the classroom helps us understand the student’s learning state and optimize the teaching and study processes. Traditional recognition methods such as self-report and teacher observation are time-consuming and obtrusive to satisfy the needs of large-scale classrooms. With the development of big data analysis and artificial intelligence, applying intelligent methods such as deep learning to recognize learning engagement has become the research hotspot in education. In this paper, based on non-invasi

APA, Harvard, Vancouver, ISO, and other styles

44

Dou, Huili, and Guohua Wang. "A Dynamic Multitarget Detection Algorithm in front of Vehicle Based on Embedded System and Internet of Things." Scientific Programming 2022 (March 20, 2022): 1–7. http://dx.doi.org/10.1155/2022/3585162.

Full text

Abstract:

There are few studies for the classification detection and dynamic multitarget detection of the targets in front of vehicles. In order to solve this problem, a dynamic multitarget detection algorithm is proposed. First, a dynamic multitarget detection with displacement at any time is suggested; secondly, a multitarget detection algorithm based on improved You Only Look Once version 3 (YOLOv3) is proposed for the detection of multitarget high probability risk events in front of the vehicle. The YOLOv3 algorithm model is a lightweight backbone network that uses embedded real-time detection techn

APA, Harvard, Vancouver, ISO, and other styles

45

Liu, Shida, Xuyun Wang, Honghai Ji, Li Wang, and Zhongsheng Hou. "A Novel Driver Abnormal Behavior Recognition and Analysis Strategy and Its Application in a Practical Vehicle." Symmetry 14, no. 10 (2022): 1956. http://dx.doi.org/10.3390/sym14101956.

Full text

Abstract:

In this work, a novel driver abnormal behavior analysis system based on practical facial landmark detection (PFLD) and you only look once version 5 (YOLOv5) were developed to solve the recognition and analysis of driver abnormal behaviors. First, a library for analyzing the abnormal behavior of vehicle drivers was designed, in which the factors that cause an abnormal behavior of drivers were divided into three categories according to the behavioral characteristics including natural behavioral factors, unnatural behavioral factors, and passive behavioral factors. Then, different neural network

APA, Harvard, Vancouver, ISO, and other styles

46

Jia, Xuejun, Xiaoxiong Zhou, Chunyi Su, et al. "High-Precision and Lightweight Model for Rapid Safety Helmet Detection." Sensors 24, no. 21 (2024): 6985. http://dx.doi.org/10.3390/s24216985.

Full text

Abstract:

This paper presents significant improvements in the accuracy and computational efficiency of safety helmet detection within industrial environments through the optimization of the you only look once version 5 small (YOLOv5s) model structure and the enhancement of its loss function. We introduce the convolutional block attention module (CBAM) to bolster the model’s sensitivity to key features, thereby enhancing detection accuracy. To address potential performance degradation issues associated with the complete intersection over union (CIoU) loss function in the original model, we implement the

APA, Harvard, Vancouver, ISO, and other styles

47

Huang, Yourui, Lingya Jiang, Tao Han, Shanyong Xu, Yuwen Liu, and Jiahao Fu. "High-Accuracy Insulator Defect Detection for Overhead Transmission Lines Based on Improved YOLOv5." Applied Sciences 12, no. 24 (2022): 12682. http://dx.doi.org/10.3390/app122412682.

Full text

Abstract:

As a key component in overhead cables, insulators play an important role. However, in the process of insulator inspection, due to background interference, small fault area, limitations of manual detection, and other factors, detection is difficult, has low accuracy, and is prone to missed detection and false detection. To detect insulator defects more accurately, the insulator defect detection algorithm based on You Only Look Once version 5 (YOLOv5) is proposed. A backbone network was built with lightweight modules to reduce network computing overhead. The small-scale network detection layer w

APA, Harvard, Vancouver, ISO, and other styles

48

Lee, Changui, and Seojeong Lee. "Evaluating the Vulnerability of YOLOv5 to Adversarial Attacks for Enhanced Cybersecurity in MASS." Journal of Marine Science and Engineering 11, no. 5 (2023): 947. http://dx.doi.org/10.3390/jmse11050947.

Full text

Abstract:

The development of artificial intelligence (AI) technologies, such as machine learning algorithms, computer vision systems, and sensors, has allowed maritime autonomous surface ships (MASS) to navigate, detect and avoid obstacles, and make real-time decisions based on their environment. Despite the benefits of AI in MASS, its potential security threats must be considered. An adversarial attack is a security threat that involves manipulating the training data of a model to compromise its accuracy and reliability. This study focuses on security threats faced by a deep neural network-based object

APA, Harvard, Vancouver, ISO, and other styles

49

Zhang, Ke. "Detection and analysis of student behavior in college labor education courses based on YOLOv5 network." Journal of Computational Methods in Sciences and Engineering 24, no. 2 (2024): 1057–69. http://dx.doi.org/10.3233/jcm-247308.

Full text

Abstract:

To improve the application of behavior detection technology in college education, the study proposes a new model built on deep CNN, which is used for student behavior detection and analysis in college labor education courses. The study first analyzed the target detection algorithm, and optimized the selected You Only Look Once version 5 (YOLOv5) algorithm and its network structure with a series of improvements, and based on this, embedded the attention module into the algorithm structure to finally obtain a new model, namely YOLOv5-O. After a series of experiments, YOLOv5-O reached an average

APA, Harvard, Vancouver, ISO, and other styles

50

Sebayang, Deo Armanta, Arya Gusman Saleh, Duman Care Khrisne, and I. Wayan Shandyasa. "RANCANG BANGUN SISTEM PENGECEKAN SAFETY HELMET DAN MASKER PADA PROYEK PEMBANGUNAN DENGAN METODELOGI YOLOv5 DENGAN MIKROKONTROLER SEBAGAI INDIKATOR." Jurnal SPEKTRUM 10, no. 4 (2023): 167. http://dx.doi.org/10.24843/spektrum.2023.v10.i04.p21.

Full text

Abstract:

The use of Personal Protective Equipment (PPE) is a very important role in occupational health and safety, especially safety helmets that can minimize the impact of falling small and large objects, besides safety helmets, masks are no less important to prevent dust and small particles from entering the respiratory tract. To ensure that field workers wear PPE, especially safety helmets and masks, a system is needed that can automatically check workers before entering the work area. This research aims to find out how to create an AI model to detect safety helmets and masks using the You Only Loo

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!