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Journal articles on the topic 'YOLO ALGORITHMS'

Author: Grafiati

Published: 16 January 2024

Last updated: 31 July 2025

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1

Tao, Wenlei. "Analysis the improvements of YOLOv5 algorithms: NRT-YOLO, MR-YOLO and YPH-YOLOv5." Applied and Computational Engineering 54, no. 1 (2024): 155–60. http://dx.doi.org/10.54254/2755-2721/54/20241466.

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Computer Vision (CV) is a fundamental aspect of artificial intelligence, with applications spanning multiple domains. The YOLO (You Only Look Once) algorithm has significantly contributed to real-time object recognition in CV. This paper explores the evolution of the YOLO algorithm, focusing on the improvements brought by three specialized variants: NRT-YOLO, MR-YOLO, and TPH-YOLOv5. NRT-YOLO addresses the challenge by introducing the C3NRT module, enhancing precision while maintaining low complexity. MR-YOLO optimizes YOLOv5 for industrial quality control, improving speed and accuracy. TPH-YO

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Liu, Liya. "Application of Multi-objective Evolutionary Algorithms for Multidimensional Sensory Data Prediction and Resource Scheduling in Smart City Design." Scalable Computing: Practice and Experience 25, no. 4 (2024): 2973–84. http://dx.doi.org/10.12694/scpe.v25i4.2929.

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Multidimensional sensory data prediction and resource scheduling are paramount challenges in the design of smart cities. This paper delves into the utilization of multi-objective evolutionary algorithms to enhance the accuracy and efficiency of target detection through optimized YOLO_v3 network models. By integrating the YOLO_v3 model with the K-means++ algorithm for Anchor_Box generation, the novel approach exhibits superior adaptability and flexibility, particularly in handling variable-sized feature pattern mappings. This adaptability better caters to the detection of targets of diverse siz

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Wan, Chengjuan, Yuxuan Pang, and Shanzhen Lan. "Overview of YOLO Object Detection Algorithm." International Journal of Computing and Information Technology 2, no. 1 (2022): 11. http://dx.doi.org/10.56028/ijcit.1.2.11.

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As an important research direction in the field of computer vision, object detection has developed  rapidly and many kinds of mature algorithms emerged. The series of YOLO (You Only Look Once)  algorithms implement one-stage detection based on regression ideas, which showing preeminent  in speed and owning strong generalization on a variety of datasets. This paper will give a simple  introduction to the current mainstream deep learning object detection algorithm, then focus on  combing the principle and optimizational process of the series of YOLO algorithms, summ

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Liang, Junbiao. "A review of the development of YOLO object detection algorithm." Applied and Computational Engineering 71, no. 1 (2024): 39–46. http://dx.doi.org/10.54254/2755-2721/71/20241642.

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The You Only Look Once (YOLO) algorithm series, as the forefront of object detection technology, has evolved from YOLOv1 to YOLOv10, consistently enhancing detection speed and accuracy. Through literature review and data analysis. This paper mainly discusses the development processes of the YOLO algorithm series, focuses on the changes and innovations in network structure, training strategies, and performance optimization. By introducing techniques, such as CSPNet, Anchor-free, data augmentation, and multi-scale training, the YOLO algorithm has progressively found a better balance between dete

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Zhong, Zixuan. "Pedestrian detection and gender recognition utilizing YOLO and CNN algorithms." Applied and Computational Engineering 31, no. 1 (2024): 133–38. http://dx.doi.org/10.54254/2755-2721/31/20230136.

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As crowd-based activities continue to surge in locales such as markets and restaurants, the significance of understanding pedestrian flow is increasingly evident. Over recent years, advancements in dynamic pedestrian detection, facilitated by the YOLO (You Only Look Once) algorithm, have seen widespread application in areas like crowd management and occupancy estimation. The YOLO algorithm has demonstrated high accuracy and efficiency in real-time object tracking and counting. However, for specific use cases, data derived solely from monitoring pedestrian flows may prove inadequate. This study

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Xu, Feifei, Yan Wan, Zhipeng Ning, and Hui Wang. "Comparative Study of Lightweight Target Detection Methods for Unmanned Aerial Vehicle-Based Road Distress Survey." Sensors 24, no. 18 (2024): 6159. http://dx.doi.org/10.3390/s24186159.

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Unmanned aerial vehicles (UAVs) are effective tools for identifying road anomalies with limited detection coverage due to the discrete spatial distribution of roads. Despite computational, storage, and transmission challenges, existing detection algorithms can be improved to support this task with robustness and efficiency. In this study, the K-means clustering algorithm was used to calculate the best prior anchor boxes; Faster R-CNN (region-based convolutional neural network), YOLOX-s (You Only Look Once version X-small), YOLOv5-s, YOLOv7-tiny, YOLO-MobileNet, and YOLO-RDD models were built b

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Zhou, Xuan, Jianping Yi, Guokun Xie, Yajuan Jia, Genqi Xu, and Min Sun. "Human Detection Algorithm Based on Improved YOLO v4." Information Technology and Control 51, no. 3 (2022): 485–98. http://dx.doi.org/10.5755/j01.itc.51.3.30540.

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The human behavior datasets have the characteristics of complex background, diverse poses, partial occlusion, and diverse sizes. Firstly, this paper adopts YOLO v3 and YOLO v4 algorithms to detect human objects in videos, and qualitatively analyzes and compares detection performance of two algorithms on UTI, UCF101, HMDB51 and CASIA datasets. Then, this paper proposed an improved YOLO v4 algorithm since the vanilla YOLO v4 has incomplete human detection in specific video frames. Specifically, the improved YOLO v4 introduces the Ghost module in the CBM module to further reduce the number of par

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Kadhum, Aseil Nadhum, and Aseel Nadhum Kadhum. "Literature Survey on YOLO Models for Face Recognition in Covid-19 Pandemic." June-July 2023, no. 34 (July 29, 2023): 27–35. http://dx.doi.org/10.55529/jipirs.34.27.35.

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Artificial Intelligence and robotics the fields in which there is necessary required object detection algorithms. In this study, YOLO and different versions of YOLO are studied to find out advantages of each model as well as limitations of each model. Even in this study, YOLO version similarities and differences are studied. Improvement in the YOLO (You Only Look Once) as well as CNN (Convolutional Neural Network) is the research study present going on for different object detection. In this paper, each YOLO version model is discussed in detail with advantages, limitations and performance. YOL

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Liu, Tao, Bo Pang, Lei Zhang, Wei Yang, and Xiaoqiang Sun. "Sea Surface Object Detection Algorithm Based on YOLO v4 Fused with Reverse Depthwise Separable Convolution (RDSC) for USV." Journal of Marine Science and Engineering 9, no. 7 (2021): 753. http://dx.doi.org/10.3390/jmse9070753.

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Unmanned surface vehicles (USVs) have been extensively used in various dangerous maritime tasks. Vision-based sea surface object detection algorithms can improve the environment perception abilities of USVs. In recent years, the object detection algorithms based on neural networks have greatly enhanced the accuracy and speed of object detection. However, the balance between speed and accuracy is a difficulty in the application of object detection algorithms for USVs. Most of the existing object detection algorithms have limited performance when they are applied in the object detection technolo

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10

Kang, Shizhao, Ziyu Hu, Lianjun Liu, Kexin Zhang, and Zhiyu Cao. "Object Detection YOLO Algorithms and Their Industrial Applications: Overview and Comparative Analysis." Electronics 14, no. 6 (2025): 1104. https://doi.org/10.3390/electronics14061104.

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Deep-learning-based object detection algorithms play a pivotal role in various domains, including face detection, automatic driving, monitoring security, and industrial production. Compared with the traditional object detection algorithms and the two-stage object detection algorithms, the YOLO (You Only Look Once) series improved the detection speed and accuracy. In addition, the YOLO series of object detection algorithms are widely used in the industrial fields due to their real-time and high-precision characteristics. This work summarizes the main versions of YOLO series algorithms as well a

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Chen, Xin, Peng Shi, and Yi Hu. "A Precise Semantic Segmentation Model for Seabed Sediment Detection Using YOLO-C." Journal of Marine Science and Engineering 11, no. 7 (2023): 1475. http://dx.doi.org/10.3390/jmse11071475.

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Semantic segmentation methods have been successfully applied in seabed sediment detection. However, fast models like YOLO only produce rough segmentation boundaries (rectangles), while precise models like U-Net require too much time. In order to achieve fast and precise semantic segmentation results, this paper introduces a novel model called YOLO-C. It utilizes the full-resolution classification features of the semantic segmentation algorithm to generate more accurate regions of interest, enabling rapid separation of potential targets and achieving region-based partitioning and precise object

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Wang, Yujin, Xueying Lin, Zhaowei Xiang, and Wen-Hao Su. "VM-YOLO: YOLO with VMamba for Strawberry Flowers Detection." Plants 14, no. 3 (2025): 468. https://doi.org/10.3390/plants14030468.

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Computer vision technology is widely used in smart agriculture, primarily because of its non-invasive nature, which avoids causing damage to delicate crops. Nevertheless, the deployment of computer vision algorithms on agricultural machinery with limited computing resources represents a significant challenge. Algorithm optimization with the aim of achieving an equilibrium between accuracy and computational power represents a pivotal research topic and is the core focus of our work. In this paper, we put forward a lightweight hybrid network, named VM-YOLO, for the purpose of detecting strawberr

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Zhou, Qinghui, Diyi Zhang, Haoshi Liu, and Yuping He. "KCS-YOLO: An Improved Algorithm for Traffic Light Detection under Low Visibility Conditions." Machines 12, no. 8 (2024): 557. http://dx.doi.org/10.3390/machines12080557.

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Autonomous vehicles face challenges in small-target detection and, in particular, in accurately identifying traffic lights under low visibility conditions, e.g., fog, rain, and blurred night-time lighting. To address these issues, this paper proposes an improved algorithm, namely KCS-YOLO (you only look once), to increase the accuracy of detecting and recognizing traffic lights under low visibility conditions. First, a comparison was made to assess different YOLO algorithms. The benchmark indicates that the YOLOv5n algorithm achieves the highest mean average precision (mAP) with fewer paramete

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14

Cong, Xiaohan, Shixin Li, Fankai Chen, Chen Liu, and Yue Meng. "A Review of YOLO Object Detection Algorithms based on Deep Learning." Frontiers in Computing and Intelligent Systems 4, no. 2 (2023): 17–20. http://dx.doi.org/10.54097/fcis.v4i2.9730.

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Object detection is a research hotspot in the field of computer vision, and YOLO series shows good performance in object detection, and has been widely used in robot vision, unmanned driving and other fields in recent years. This paper first introduces the YOLO series algorithm, including the principle, innovation points, advantages and disadvantages of various algorithms, then introduces the application field of YOLO series, and finally analyzes its future development trend to provide reference for the topic research.

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15

Karmakar, Malay. "Face Recognition Technique using YOLO V5 Algorithm." International Research Journal of Computer Science 10, no. 03 (2023): 04–12. http://dx.doi.org/10.26562/irjcs.2023.v1002.01.

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In today’s date real world application like human-machine interaction, security surveillance face recognition has made its great importance. For face recognition the steps to be followed are data collection, preprocessing, Feature Extraction, Training Evaluation and finally testing. One of the best Algorithms used for face recognition is Viola-Jones Algorithm. Viola Jones Algorithm is highly accepted because of its fast processing time and high detection rate. The other detection Algorithms which can be used are HOG Algorithm (Histogram Oriented Gradient), Deep Learning CNN (Convolution Neural

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Gao, Ruizhen, Shuai Zhang, Haoqian Wang, Jingjun Zhang, Hui Li, and Zhongqi Zhang. "The Aeroplane and Undercarriage Detection Based on Attention Mechanism and Multi-Scale Features Processing." Mobile Information Systems 2022 (September 19, 2022): 1–12. http://dx.doi.org/10.1155/2022/2582288.

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Undercarriage device is one of the essential parts of an aeroplane, and accurate detection of whether the aeroplane undercarriage is operating normally can effectively avoid aeroplane accidents. To address the problems of low automation and low accuracy of small target detection in existing aeroplane undercarriage detection methods, an improved algorithm for aeroplane undercarriage detection YOLO V4 is proposed. Firstly, the convolutional network structure of Inception-ResNet is integrated into the CSPDarkNet53 framework to improve the algorithm’s ability to extract semantic information of tar

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Liu, Jiayi, Xingfei Zhu, Xingyu Zhou, Shanhua Qian, and Jinghu Yu. "Defect Detection for Metal Base of TO-Can Packaged Laser Diode Based on Improved YOLO Algorithm." Electronics 11, no. 10 (2022): 1561. http://dx.doi.org/10.3390/electronics11101561.

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Defect detection is an important part of the manufacturing process of mechanical products. In order to detect the appearance defects quickly and accurately, a method of defect detection for the metal base of TO-can packaged laser diode (metal TO-base) based on the improved You Only Look Once (YOLO) algorithm named YOLO-SO is proposed in this study. Firstly, convolutional block attention mechanism (CBAM) module was added to the convolutional layer of the backbone network. Then, a random-paste-mosaic (RPM) small object data augmentation module was proposed on the basis of Mosaic algorithm in YOL

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18

Wu, Wentong, Han Liu, Lingling Li, et al. "Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image." PLOS ONE 16, no. 10 (2021): e0259283. http://dx.doi.org/10.1371/journal.pone.0259283.

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This exploration primarily aims to jointly apply the local FCN (fully convolution neural network) and YOLO-v5 (You Only Look Once-v5) to the detection of small targets in remote sensing images. Firstly, the application effects of R-CNN (Region-Convolutional Neural Network), FRCN (Fast Region-Convolutional Neural Network), and R-FCN (Region-Based-Fully Convolutional Network) in image feature extraction are analyzed after introducing the relevant region proposal network. Secondly, YOLO-v5 algorithm is established on the basis of YOLO algorithm. Besides, the multi-scale anchor mechanism of Faster

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Li, Zhuang, Jianhui Yuan, Guixiang Li, et al. "RSI-YOLO: Object Detection Method for Remote Sensing Images Based on Improved YOLO." Sensors 23, no. 14 (2023): 6414. http://dx.doi.org/10.3390/s23146414.

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With the continuous development of deep learning technology, object detection has received extensive attention across various computer fields as a fundamental task of computational vision. Effective detection of objects in remote sensing images is a key challenge, owing to their small size and low resolution. In this study, a remote sensing image detection (RSI-YOLO) approach based on the YOLOv5 target detection algorithm is proposed, which has been proven to be one of the most representative and effective algorithms for this task. The channel attention and spatial attention mechanisms are use

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Li, Guangbo, Rui Jian, Xie Jun, and Guolong Shi. "A Review of You Only Look Once Algorithms in Animal Phenotyping Applications." Animals 15, no. 8 (2025): 1126. https://doi.org/10.3390/ani15081126.

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Animal phenotyping recognition is a pivotal component of precision livestock management, holding significant importance for intelligent farming practices and animal welfare assurance. In recent years, with the rapid advancement of deep learning technologies, the YOLO algorithm—as the pioneering single-stage detection framework—has revolutionized the field of object detection through its efficient and rapid approach and has been widely applied across various agricultural domains. This review focuses on animal phenotyping as the research target structured around four key aspects: (1) the evoluti

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Wang, Feilong, Xiaobing Yang, and Juan Wei. "YOLO-ESL: An Enhanced Pedestrian Recognition Network Based on YOLO." Applied Sciences 14, no. 20 (2024): 9588. http://dx.doi.org/10.3390/app14209588.

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Pedestrian detection is a critical task in computer vision; however, mainstream algorithms often struggle to achieve high detection accuracy in complex scenarios, particularly due to target occlusion and the presence of small objects. This paper introduces a novel pedestrian detection algorithm, YOLO-ESL, based on the YOLOv7 framework. YOLO-ESL integrates the ELAN-SA module, designed to enhance feature extraction, with the LGA module, which improves feature fusion. The ELAN-SA module optimizes the flexibility and efficiency of small object feature extraction, while the LGA module effectively i

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Hassan, Salam Abdul-Ameer, Jaleel Hassan Hassan, and Hameedi Abdullah Salma. "Development smart eyeglasses for visually impaired people based on you only look once." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 20, no. 1 (2022): 109–17. https://doi.org/10.12928/telkomnika.v20i1.22457.

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Visually impaired people are facing many problems in their life. One of these problems is how they can find the objects in their indoor environment. This research was presented to assists visually impaired people in finding the objects in office. Object detection is a method used to detect the objects in images and videos. Many algorithms used for object detection such as convolutional neural network (CNN) and you only look once (YOLO). The proposed method was YOLO which outperforms the other algorithms such as CNN. In CNN the algorithm splits the image into regions. These regions sequentially

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Ghansham, More Omkar Patil Omkar More Mihir More Samadhan Suryavanshi Manisha Mali. "Comparison of Object Detection Algorithms CNN, YOLO and SSD." International Journal of Scientific Research and Technology 1, no. 11 (2024): 137–44. https://doi.org/10.5281/zenodo.14186397.

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Since 2015, numerous studies have concentrated on object detection, a crucial element of computer vision, using convolutional neural networks (CNN) and their various architectures. Key methods for object detection done by “YOLO (You Only Look Once)”, “CNN”, and “SSD (Single Shot Multibox Detector)”. This paper explores three representative series of methods based on “CNN, YOLO, and SSD”, providing solutions to challenges like bounding box prediction in CNNs. The strength of these algorithms are measured in terms of accuracy, processing speed, and

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Yang, Chen, Jian Xiang, Xiaoyong Li, and Yunjie Xie. "FishDet-YOLO: Enhanced Underwater Fish Detection with Richer Gradient Flow and Long-Range Dependency Capture through Mamba-C2f." Electronics 13, no. 18 (2024): 3780. http://dx.doi.org/10.3390/electronics13183780.

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The fish detection task is an essential component of marine exploration, which helps scientists monitor fish population numbers and diversity and understand changes in fish behavior and habitat. It also plays a significant role in assessing the health of marine ecosystems, formulating conservation measures, and maintaining biodiversity. However, there are two main issues with current fish detection algorithms. First, the lighting conditions underwater are significantly different from those on land. In addition, light scattering and absorption in water trigger uneven illumination, color distort

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Dewanto, Muhammad Ridho, Mifta Nur Farid, Muhammad Abby Rafdi Syah, Aji Akbar Firdaus, and Hamzah Arof. "YOLO vs. CNN Algorithms: A Comparative Study in Masked Face Recognition." Scientific Journal of Informatics 11, no. 1 (2024): 139–46. http://dx.doi.org/10.15294/sji.v11i1.48723.

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Purpose: This research investigates the effectiveness of YOLO (You Only Look Once) and Convolutional Neural Network (CNN) in real-time face mask recognition, addressing the challenges posed by mask-wearing in infectious disease prevention.Method: Utilizing a diverse dataset and employing YOLO's object detection and a combined Haar Cascade Algorithm with CNN, the study evaluated key performance indicators, including accuracy, framerate, and F1 Score.Results: Results indicated that CNN outperformed YOLO in accuracy (99.3% vs. 79.3%) but operated at a slightly lower framerate. YOLO excelled in re

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Ghahremani, Ali, Scott D. Adams, Michael Norton, Sui Yang Khoo, and Abbas Z. Kouzani. "Detecting Defects in Solar Panels Using the YOLO v10 and v11 Algorithms." Electronics 14, no. 2 (2025): 344. https://doi.org/10.3390/electronics14020344.

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Solar panels play a crucial role in producing renewable electricity power for the grid, and this role grows more significant each year. However, defects in solar panels can significantly drop power output, leading to grid instability. Therefore, employing an efficient Artificial Intelligence (AI) algorithm to autonomously detect defects in solar panels is crucial. In this study, we employ the You Only Look Once (YOLO) v9, v10, and v11 algorithms to detect defects in solar panels. To this end, we examined their performance results via training on three datasets. The first dataset includes 191 t

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Li, Yanyi, Jian Wang, Jin Huang, and Yuping Li. "Research on Deep Learning Automatic Vehicle Recognition Algorithm Based on RES-YOLO Model." Sensors 22, no. 10 (2022): 3783. http://dx.doi.org/10.3390/s22103783.

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With the introduction of concepts such as ubiquitous mapping, mapping-related technologies are gradually applied in autonomous driving and target recognition. There are many problems in vision measurement and remote sensing, such as difficulty in automatic vehicle discrimination, high missing rates under multiple vehicle targets, and sensitivity to the external environment. This paper proposes an improved RES-YOLO detection algorithm to solve these problems and applies it to the automatic detection of vehicle targets. Specifically, this paper improves the detection effect of the traditional YO

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Wei, Jian, Qinzhao Wang, and Zixu Zhao. "YOLO-G: Improved YOLO for cross-domain object detection." PLOS ONE 18, no. 9 (2023): e0291241. http://dx.doi.org/10.1371/journal.pone.0291241.

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Cross-domain object detection is a key problem in the research of intelligent detection models. Different from lots of improved algorithms based on two-stage detection models, we try another way. A simple and efficient one-stage model is introduced in this paper, comprehensively considering the inference efficiency and detection precision, and expanding the scope of undertaking cross-domain object detection problems. We name this gradient reverse layer-based model YOLO-G, which greatly improves the object detection precision in cross-domain scenarios. Specifically, we add a feature alignment b

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Yang, Zhonglai. "Intelligent Recognition of Traffic Signs Based on Improved YOLO v3 Algorithm." Mobile Information Systems 2022 (September 20, 2022): 1–11. http://dx.doi.org/10.1155/2022/7877032.

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In recent years, assisted driving and autonomous driving technology have been paid more attention to by the public. Road sign recognition is of great practical significance for the realization of auto-driving technology. In the actual traffic environment, the traffic signs have the problems of small detectable volume, low resolution, unclear characteristics, and easy to be disturbed by the environment. In order to better realize road traffic sign recognition, this paper improves and optimizes the YOLO v3 network derived from YOLO v3 structure algorithm, enhances the data of the traffic signs b

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Guliutin, Nikolai, and Oleslav Antamoshkin. "Enhancing unmanned aerial vehicle capabilities: integrating YOLO algorithms for diverse industrial applications." ITM Web of Conferences 59 (2024): 03012. http://dx.doi.org/10.1051/itmconf/20245903012.

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The integration of UAVs with advanced deep learning algorithms, particularly the You Only Look Once models, has opened new horizons in various industries. This paper explores the transformative impact of YOLO-based systems across diverse sectors, including agriculture, forest fire detection, ecology, marine science, target detection, and UAV navigation. We delve into the specific applications of different YOLO models, ranging from YOLOv3 to the lightweight YOLOv8, highlighting their unique contributions to enhancing UAV functionalities. In agriculture, UAVs equipped with YOLO algorithms have r

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Hu, Xiao, Shenfu Pan, Dongdong Li, Long Feng, and Yuan Zhao. "An airborne object detection and location system based on deep inference." Journal of Physics: Conference Series 2632, no. 1 (2023): 012019. http://dx.doi.org/10.1088/1742-6596/2632/1/012019.

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Abstract In recent years, with the development of sensors, communication networks, and deep learning, drones have been widely used in the field of object detection, tracking, and positioning. However, there are inefficient task execution and some complex algorithms still need to rely on large servers, which is intolerable in rescue and traffic scheduling tasks. Designing fast algorithms that can run on the airborne computer can effectively solve the problem. In this paper, an object detection and location system for drones is proposed. We combine the improved object detection algorithm ST-YOLO

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He, Guowen, Wenlong Wang, Bowen Shi, Shijie Liu, Hui Xiang, and Xiaoyuan Wang. "An Improved YOLO v4 Algorithm-based Object Detection Method for Maritime Vessels." International Journal of Science and Engineering Applications 11, no. 04 (2022): 50–55. http://dx.doi.org/10.7753/ijsea1104.1001.

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Ship object detection is the core part of the maritime intelligent ship safety assistance technology, which plays a crucial role in ship safety. The object detection algorithm based on the convolutional neural network has greatly improved the accuracy and speed of object detection, which YOLO algorithm stands out among the object detection algorithms with more excellent robustness, detection accuracy, and real-time performance. Based on the YOLO v4 algorithm, this study uses the k-means algorithm to improve clustering at the input side of image data and introduces relevant berth data in the se

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Asif, Muhammad, Tabarka Rajab, Samreen Hussain, et al. "Performance Evaluation of Deep Learning Algorithm Using High-End Media Processing Board in Real-Time Environment." Journal of Sensors 2022 (December 7, 2022): 1–13. http://dx.doi.org/10.1155/2022/6335118.

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Image processing-based artificial intelligence algorithm is a critical task, and the implementation requires a careful examination for the selection of the algorithm and the processing unit. With the advancement of technology, researchers have developed many algorithms to achieve high accuracy at minimum processing requirements. On the other hand, cost-effective high-end graphical processing units (GPUs) are now available to handle complex processing tasks. However, the optimum configurations of the various deep learning algorithms implemented on GPUs are yet to be investigated. In this propos

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Dewi, Christine, and Henoch Juli Christanto. "Combination of Deep Cross-Stage Partial Network and Spatial Pyramid Pooling for Automatic Hand Detection." Big Data and Cognitive Computing 6, no. 3 (2022): 85. http://dx.doi.org/10.3390/bdcc6030085.

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The human hand is involved in many computer vision tasks, such as hand posture estimation, hand movement identification, human activity analysis, and other similar tasks, in which hand detection is an important preprocessing step. It is still difficult to correctly recognize some hands in a cluttered environment because of the complex display variations of agile human hands and the fact that they have a wide range of motion. In this study, we provide a brief assessment of CNN-based object identification algorithms, specifically Densenet Yolo V2, Densenet Yolo V2 CSP, Densenet Yolo V2 CSP SPP,

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Chen, Bo. "Research Overview of YOLO Series Object Detection Algorithms Based on Deep Learning." Journal of Computing and Electronic Information Management 15, no. 3 (2024): 84–92. https://doi.org/10.54097/p81rtv77.

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In the rapid development of deep learning, YOLO, as the first popular single-stage object detection model, has sparked an innovative storm in the computer vision community with its remarkable architecture and innovative concepts, marking a significant leap forward in object detection technology. Today, it is not only regarded as a milestone in this field but also sets an unparalleled example in the pursuit of the perfect fusion of detection speed and accuracy. YOLO has been widely applied in various fields such as agriculture, industry, pedestrian detection, and more. This research project wil

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Rodriguez-Guillen, Reinier, John Kern, and Claudio Urrea. "Fast Rock Detection in Visually Contaminated Mining Environments Using Machine Learning and Deep Learning Techniques." Applied Sciences 14, no. 2 (2024): 731. http://dx.doi.org/10.3390/app14020731.

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Advances in machine learning algorithms have allowed object detection and classification to become booming areas. The detection of objects, such as rocks, in mining operations is affected by fog, snow, suspended particles, and high lighting. These environmental conditions can stop the development of mining work, which entails a considerable increase in operating costs. It is vital to select a machine learning algorithm that is accurate, fast, and contributes to lower operational costs because of the aforementioned environmental situations. In this study, the Viola-Jones algorithm, Aggregate Ch

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Yang, Fayu, Xuejiao Duan, Jia Wang, Yueming Wang, and Hong-an Zhang. "Research and application of target detection algorithm for live operation in substation." Journal of Physics: Conference Series 2703, no. 1 (2024): 012038. http://dx.doi.org/10.1088/1742-6596/2703/1/012038.

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Abstract The substation is an important node connecting the backbone network and distribution network and is the core hub of the smart grid. With the continuous increase of electrical equipment, the substation plays a vital role in the whole power system. The structural principle of deep learning convolutional neural network is deeply studied, which lays a theoretical foundation for substation fault detection and recognition based on the object detection algorithm. This paper focuses on fault detection algorithms based on deep learning. First, two mainstream target detection algorithms, Faster

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Guo, Hao, and Jiahua Yang. "An improved object detection algorithm based on YOLO*." Journal of Physics: Conference Series 2216, no. 1 (2022): 012070. http://dx.doi.org/10.1088/1742-6596/2216/1/012070.

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Abstract Accuracy and speed have always been a measure of the performance of object detection algorithms. The current algorithms have reduced the detection speed on the basis of increasing a certain accuracy due to their complex structure. In response to this problem, this paper uses the RepVGG network to improve the original YOLOv3 structure, which uses a diversified branch structure to enhance the network feature extraction ability during training and transforms the training model into an equivalent VGG-like topology network model during inference. In addition, we use ASFF to deal with the p

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Yin, Zijuan, Haichao Li, Bo Qi, and Guangyue Shan. "BBW YOLO: Intelligent Detection Algorithms for Aluminium Profile Material Surface Defects." Coatings 15, no. 6 (2025): 684. https://doi.org/10.3390/coatings15060684.

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This study aims to address the issue of various defects on the surface of aluminum profile materials, which can significantly impact industrial production as well as the reliability and safety of products. An algorithmic model, BBW YOLO (YOLOv8-BiFPN-BiFormer-WIoU v3), based on an enhanced YOLOv8 model is proposed for aluminum profile material surface-defect detection. First, the model can effectively eliminate redundant feature information and enhance the feature-extraction process by incorporating a weighted Bidirectional Feature Pyramid Feature-fusion Network (BiFPN). Second, the model inco

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Wang, Chunli, Long Xiangyu, Chu Zhongmin, and Pan Mingfang. "Comparative Study and Analysis of Different Target Detection Algorithms in Traffic Sign Detection." Journal of Engineering System 1, no. 2 (2023): 73–79. http://dx.doi.org/10.62517/jes.202302213.

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The advancement of artificial intelligence in transportation has led to a burgeoning interest in the research of automatic identification technologies, particularly in the realm of traffic signs. It is an important pioneer technology of unmanned driving technology and has great theoretical value and application prospect. However, traffic sign detection is faced with the influence of complex weather factors such as rain, snow and fog, as well as the problem that the target is partially blocked and the size of the target is very small. Hence, selecting a target detection algorithm capable of swi

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Lamsadi, Setyanto Arief, and Hidayat Tonny. "Systematic Literature Review of Pedestrian Detection using the YOLO Algorithm." International Journal of Innovative Science and Research Technology 8, no. 5 (2023): 1420–24. https://doi.org/10.5281/zenodo.7982422.

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Technology is developing so rapidly at this time. Every time various latest and cutting-edge technologies in various fields transmit life. One of them is in the field of object detection. As technology develops, the need for object detection systems becomes very strong. Object detection or object detection is the lifeblood of Computer Vision and Image Processing. There are 4 main focuses in Computer Vision, namely Recognition, Visual Tracking (visual tracking), Semantic Segmentation (semantic segmentation) and Image Restoration (image restoration). To be able to do these four things, we need a

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Nori, Ruba R., Rabah N. Farhan, and Safaa Hussein Abed. "Indoor and Outdoor Fire Localization Using YOLO Algorithm." Journal of Physics: Conference Series 2114, no. 1 (2021): 012067. http://dx.doi.org/10.1088/1742-6596/2114/1/012067.

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Abstract Novel algorithm for fire detection has been introduced. CNN based System localization of fire for real time applications was proposed. Deep learning algorithms shows excellent results in a way that it accuracy reaches very high accuracy for fire image dataset. Yolo is a superior deep learning algorithm that is capable of detect and localize fires in real time. The luck of image dataset force us to limit the system in binary classification test. Proposed model was tested on dataset gathered from the internet. In this article, we built an automated alert system integrating multiple sens

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Mou, Chao, Tengfei Liu, Chengcheng Zhu, and Xiaohui Cui. "WAID: A Large-Scale Dataset for Wildlife Detection with Drones." Applied Sciences 13, no. 18 (2023): 10397. http://dx.doi.org/10.3390/app131810397.

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Drones are widely used for wildlife monitoring. Deep learning algorithms are key to the success of monitoring wildlife with drones, although they face the problem of detecting small targets. To solve this problem, we have introduced the SE-YOLO model, which incorporates a channel self-attention mechanism into the advanced real-time object detection algorithm YOLOv7, enabling the model to perform effectively on small targets. However, there is another barrier; the lack of publicly available UAV wildlife aerial datasets hampers research on UAV wildlife monitoring algorithms. To fill this gap, we

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Hao, Junbo, Guangying Yan, Lidong Wang, Honglan Pei, Xu Xiao, and Baifu Zhang. "A Lightweight Transmission Line Foreign Object Detection Algorithm Incorporating Adaptive Weight Pooling." Electronics 13, no. 23 (2024): 4645. http://dx.doi.org/10.3390/electronics13234645.

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Aerial photography using unmanned aerial vehicles (UAVs) to detect foreign objects is an important method to ensure the safety of transmission lines. However, existing detection algorithms often encounter challenges in complex environments, including limited recognition capability and high computational demands. To address these issues, this paper proposes YOLO-LAF, a lightweight foreign object detection algorithm that is based on YOLOv8n and incorporates an innovative adaptive weight pooling technique. The proposed method introduces a novel adaptive weight pooling module within the backbone n

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Liu, Yu, Tong Zhou, Jingye Xu, Yu Hong, Qianhui Pu, and Xuguang Wen. "Rotating Target Detection Method of Concrete Bridge Crack Based on YOLO v5." Applied Sciences 13, no. 20 (2023): 11118. http://dx.doi.org/10.3390/app132011118.

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Crack detection is a critical and essential aspect of concrete bridge maintenance and management. Manual inspection often falls short in meeting the demands of large-scale crack detection in terms of cost, efficiency, accuracy, and data management. To address the challenges faced by existing generic object detection algorithms in achieving high accuracy or efficiency when detecting cracks with large aspect ratios, overlapping structures, and clear directional characteristics, this paper presents improvements to the YOLO v5 model. These enhancements include the introduction of angle regression

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Xu, Danqing, and Yiquan Wu. "MRFF-YOLO: A Multi-Receptive Fields Fusion Network for Remote Sensing Target Detection." Remote Sensing 12, no. 19 (2020): 3118. http://dx.doi.org/10.3390/rs12193118.

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High-altitude remote sensing target detection has problems related to its low precision and low detection rate. In order to enhance the performance of detecting remote sensing targets, a new YOLO (You Only Look Once)-V3-based algorithm was proposed. In our improved YOLO-V3, we introduced the concept of multi-receptive fields to enhance the performance of feature extraction. Therefore, the proposed model was termed Multi-Receptive Fields Fusion YOLO (MRFF-YOLO). In addition, to address the flaws of YOLO-V3 in detecting small targets, we increased the detection layers from three to four. Moreove

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Zhao, Jing. "A Real-Time Detection Algorithm of Flame Target Image." Computational Intelligence and Neuroscience 2022 (October 11, 2022): 1–8. http://dx.doi.org/10.1155/2022/5277805.

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In many research tasks, the speed and accuracy of flame detection using supply chain have always been a challenging task for many researchers, especially for flame detection of small objects in supply chain. In view of this, we propose a new real-time target detection algorithm. The first step is to enhance the flame recognition of small objects by strengthening the feature extraction ability of multi-scale fusion. The second step is to introduce the K-means clustering method into the prior bounding box of the algorithm to improve the accuracy of the algorithm. The third step is to use the fla

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Sun, Chen, Yihong Zhang, and Shuai Ma. "DFLM-YOLO: A Lightweight YOLO Model with Multiscale Feature Fusion Capabilities for Open Water Aerial Imagery." Drones 8, no. 8 (2024): 400. http://dx.doi.org/10.3390/drones8080400.

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Object detection algorithms for open water aerial images present challenges such as small object size, unsatisfactory detection accuracy, numerous network parameters, and enormous computational demands. Current detection algorithms struggle to meet the accuracy and speed requirements while being deployable on small mobile devices. This paper proposes DFLM-YOLO, a lightweight small-object detection network based on the YOLOv8 algorithm with multiscale feature fusion. Firstly, to solve the class imbalance problem of the SeaDroneSee dataset, we propose a data augmentation algorithm called Small O

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Chitraningrum, Nidya, Lies Banowati, Dina Herdiana, et al. "Comparison Study of Corn Leaf Disease Detection based on Deep Learning YOLO-v5 and YOLO-v8." Journal of Engineering and Technological Sciences 56, no. 1 (2024): 61–70. http://dx.doi.org/10.5614/j.eng.technol.sci.2024.56.1.5.

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Corn is one of the primary carbohydrate-rich food commodities in Southeast Asian countries, among which Indonesia. Corn production is highly dependent on the health of the corn plant. Infected plants will decrease corn plant productivity. Usually, corn farmers use conventional methods to control diseases in corn plants. Still, these methods are not effective and efficient because they require a long time and a lot of human labor. Deep learning-based plant disease detection has recently been used for early disease detection in agriculture. In this work, we used convolutional neural network algo

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Kadhum, Aseil Nahum, and Aseel Nahum Kadhum. "Comparison Between the Yolov4 and Yolov5 Models in Detecting Faces while Wearing a Mask." International Academic Journal of Science and Engineering 11, no. 1 (2024): 01–08. http://dx.doi.org/10.9756/iajse/v11i1/iajse1101.

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Object detection based on deep learning has shown good results ever since the Coronavirus or Covid-19 started sweeping the entire world affecting and killing many people. One of the easiest and simplest ways to protect oneself from this virus is to wear a mask. In order to detect whether a person is wearing a mask or not, we propose here two models for detecting face masks. Facial recognition has been difficult, but with the development of deep learning, it has tremendous ability to detect objects, especially in public places. Therefore, it has become necessary for accurate diagnosis to protec

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