Suchergebnisse - YOLO-R object detection algorithm*

Leveraging YOLOv9 for Advanced Colorectal Polyp Detection.

Autoren: Haider, Zeeshan Azar, Ahmad Taher ALmutlaq, Samah

Quelle: Engineering, Technology & Applied Science Research; Oct2025, Vol. 15 Issue 5, p27629-27639, 11p

Schlagwörter: COLON polyps, OBJECT recognition (Computer vision), DETECTION algorithms, COMPUTER-aided diagnosis

YOLO: A Competitive Analysis of Modern Object Detection Algorithms for Road Defects Detection Using Drone Images.

Alternate Title: YOLO: تحليل تنافسي لخوارزميات الكشف عن األشياء الحديثة للكشف عن عيوب الطرق باستخدام صور الطائرات بدون طيار. (Arabic)

Autoren: Sadhin, Amit Hasan Mohd Hashim, Siti Zaiton Samma, Hussein et al.

Quelle: Baghdad Science Journal; 2024, Vol. 21 Issue 6, p2167-2181, 15p

Schlagwörter: OBJECT recognition (Computer vision), CONVOLUTIONAL neural networks, DETECTION algorithms, COMPUTER vision, TRANSPORTATION safety measures, INFRASTRUCTURE (Economics), TECHNOLOGICAL innovations

A precise crop row detection algorithm in complex farmland for unmanned agricultural machines.

Autoren: Ruan, Zhiwen Chang, Penghao Cui, Shangqing et al.

Quelle: Biosystems Engineering. Aug2023, Vol. 232, p1-12. 12p.

Schlagwörter: *AGRICULTURE, *OBJECT recognition (Computer vision), *CROPS, *LEAST squares, *ALGORITHMS

A Review of Object Detection Techniques in IoT-Based Intelligent Transportation Systems.

Autoren: Wang, Jiaqi Su, Jian

Quelle: Computers, Materials & Continua; 2025, Vol. 84 Issue 1, p125-152, 28p

Schlagwörter: OBJECT recognition (Computer vision), DATA privacy, TRAFFIC monitoring, TRAFFIC congestion, TRAFFIC signs & signals, INTELLIGENT transportation systems, TRAFFIC safety

Analyzing Real-Time Object Detection with YOLO Algorithm in Automotive Applications: A Review.

Autoren: Gheorghe, Carmen Duguleana, Mihai Boboc, Razvan Gabriel et al.

Quelle: Computer Modeling in Engineering & Sciences (CMES); 2024, Vol. 141 Issue 3, p1939-1981, 43p

Schlagwörter: OBJECT recognition (Computer vision), BIBLIOMETRICS, IMAGE processing, MOTOR vehicles, AUTONOMOUS vehicles

YOLO advances to its genesis: a decadal and comprehensive review of the You Only Look Once (YOLO) series.

Autoren: Sapkota, Ranjan Flores-Calero, Marco Qureshi, Rizwan et al.

Quelle: Artificial Intelligence Review; Sep2025, Vol. 58 Issue 9, p1-83, 83p

Curved text detection and recognition in natural scenic images based on L-Yolov8 object detection algorithm.

Autoren: Chhabra, Prachi Abidi, Ali Imam

Quelle: Signal, Image & Video Processing; Apr2025, Vol. 19 Issue 4, p1-10, 10p

多尺度特征聚合扩散和边缘信息增强的小目标检测算法. (Chinese)

Alternate Title: Multi-Scale Feature Aggregation Diffusion and Edge Information Enhancement Small Object Detection Algorithm. (English)

Autoren: 江旺玉 王乐 姚叶鹏 et al.

Quelle: Journal of Computer Engineering & Applications; Apr2025, Vol. 61 Issue 7, p105-116, 12p

Multi-class Object Detection Algorithm Based on Convolutional Neural Network

Autoren: Yanjuan Wang Wang Xiao Haijun Niu et al.

Quelle: 2019 IEEE International Conference of Intelligent Applied Systems on Engineering (ICIASE). :286-289

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=9074015
http://xplorestaging.ieee.org/ielx7/9055908/9074005/09074015.pdf?arnumber=9074015

Deep learning – enabled visual computing in construction: application and digital technology integration.

Autoren: Perera, Prasad Perera, Srinath Jin, Xiaohua et al.

Quelle: Frontiers in Built Environment; 2025, p1-15, 15p

Schlagwörter: DEEP learning, BUILDING design & construction, VISUAL perception, TECHNOLOGY convergence, CONSTRUCTION industry safety, OBJECT recognition (Computer vision), ARTIFICIAL intelligence

Implementation of YOLO v7 algorithm in estimating traffic flow in Malang.

Autoren: Saputri, Hanis Amalia Avrillio, Matthew Christofer, Louis et al.

Quelle: Procedia Computer Science. 2024, Vol. 245, p117-126. 10p.

Schlagwörter: Object recognition (Computer vision), Traffic estimation, Traffic flow, Intelligent transportation systems, Machine learning, Traffic monitoring

Object Detection Using CNN

Autoren: P.Bhaskar Naidu Pulakanam Anusha Gothula Naveena et al.

Quelle: International Journal of Innovative Science and Research Technology (IJISRT). :632-637

An Evaluation of Image Slicing and YOLO Architectures for Object Detection in UAV Images.

Autoren: Telçeken, Muhammed Akgun, Devrim Kacar, Sezgin

Quelle: Applied Sciences (2076-3417); Dec2024, Vol. 14 Issue 23, p11293, 16p

Schlagwörter: DRONE aircraft, PIXELS

An improved deep learning method for flying object detection and recognition.

Autoren: Aote, Shailendra S. Wankhade, Nisha Pardhi, Aniket et al.

Quelle: Signal, Image & Video Processing; Feb2024, Vol. 18 Issue 1, p143-152, 10p

Multi-class fruit ripeness detection using YOLO and SSD object detection models.

Autoren: Kamat, Pooja Gite, Shilpa Chandekar, Harsh et al.

Quelle: Discover Applied Sciences; Sep2025, Vol. 7 Issue 9, p1-17, 17p

YH-RTYO: an end-to-end object detection method for crop growth anomaly detection in UAV scenarios.

Autoren: Li, Yihang Yang, WenZhong Lu, Zhifeng et al.

Quelle: PeerJ Computer Science; Dec2024, p1-30, 30p

Schlagwörter: OBJECT recognition (Computer vision), AGRICULTURAL drones, DRONE aircraft, CROP growth, MODELS & modelmaking

Hybrid Backbone-Based Deep Learning Model for Early Detection of Forest Fire Smoke.

Autoren: Çınarer, Gökalp

Quelle: Applied Sciences (2076-3417); Jul2025, Vol. 15 Issue 13, p7178, 24p

Schlagwörter: FOREST fires, DEEP learning, FEATURE extraction, CONVOLUTIONAL neural networks, OBJECT recognition (Computer vision), AEROSOL analysis, REMOTE sensing, ENVIRONMENTAL monitoring

Pseudo Trained YOLO R_CNN Model for Weapon Detection with a Real-Time Kaggle Dataset

Autoren: A, Ratna Raju Maddileti, Telugu J., Sirisha et al.

Quelle: International Journal of Integrated Engineering; Vol. 14 No. 7 (2022): Special Issue: Electrical Engineering; 131-145; 2600-7916; 2229-838X

Index Begriffe: Object detection, surveillance, computer vision, deep learning, YOLO, R-CNN., info:eu-repo/semantics/article, info:eu-repo/semantics/publishedVersion

URL: http://penerbit.uthm.edu.my/ojs/index.php/ijie/article/view/10842
http://penerbit.uthm.edu.my/ojs/index.php/ijie/article/view/10842/5548
http://penerbit.uthm.edu.my/ojs/index.php/ijie/article/view/10842/5548

Advancements in real-time road damage detection: a comprehensive survey of methodologies and datasets.

Autoren: Abdelwahed, Salma H. Sharobim, Bishoy K. Wasfey, Bishoy et al.

Quelle: Journal of Real-Time Image Processing; Aug2025, Vol. 22 Issue 4, p1-13, 13p

Spatially Adaptive and Distillation-Enhanced Mini-Patch Attacks for Remote Sensing Image Object Detection.

Autoren: Yang, Zhihan Li, Xiaohui Zhang, Linchao et al.

Quelle: Electronics (2079-9292); Sep2025, Vol. 14 Issue 17, p3433, 23p

Schlagwörter: REMOTE sensing, ARTIFICIAL neural networks, OBJECT recognition (Computer vision), KNOWLEDGE transfer