YOLO Model

YOLO (You Only Look Once) models are a family of real-time object detection algorithms used extensively in computer vision. Current research focuses on improving YOLO's accuracy and efficiency across diverse applications, including agricultural automation, search and rescue, industrial monitoring, and assistive technologies for the visually impaired, with variations like YOLOv5, YOLOv8, and YOLOv7 being prominent. These improvements involve optimizing model architectures (e.g., incorporating attention mechanisms, lightweight backbones), enhancing training techniques (e.g., data augmentation, quantization-aware training), and adapting the models for resource-constrained devices. The resulting advancements have significant implications for various fields, enabling faster and more accurate object detection in real-world scenarios.

Papers

January 30, 2023

YOLO-based Object Detection in Industry 4.0 Fischertechnik Model Environment
Slavomira Schneidereit, Ashkan Mansouri Yarahmadi, Toni Schneidereit, Michael Breuß, Marc Gebauer
Industry Assignment YOLO Object YOLO Model Manufacturing Environment Object Correlation

December 18, 2022

Performance Analysis of YOLO-based Architectures for Vehicle Detection from Traffic Images in Bangladesh
Refaat Mohammad Alamgir, Ali Abir Shuvro, Mueeze Al Mushabbir, Mohammed Ashfaq Raiyan, Nusrat Jahan Rani, Md. Mushfiqur Rahman, Md. Hasanul Kabir, Sabbir Ahmed
Performance Analysis Vehicle Detection YOLO Model Traffic Surveillance Vehicle Re Id

March 9, 2022

Evaluation of YOLO Models with Sliced Inference for Small Object Detection
Muhammed Can Keles, Batuhan Salmanoglu, Mehmet Serdar Guzel, Baran Gursoy, Gazi Erkan Bostanci
Global Evaluation Object Detection Model YOLOv5 Model Small Object Detection Fire YOLOv5 Attains YOLO Model

December 17, 2021

AI-Assisted Verification of Biometric Data Collection
Ryan Lindsey
Source Video Verification Task YOLO Model

November 4, 2021

Tea Chrysanthemum Detection under Unstructured Environments Using the TC-YOLO Model
Chao Qi, Junfeng Gao, Simon Pearson, Helen Harman, Kunjie Chen, Lei Shu
Neural Network Unstructured Environment YOLO Model Selective Harvesting Selective Chrysanthemum Harvesting Robot

YOLO Model

Papers

YOLO-based Object Detection in Industry 4.0 Fischertechnik Model Environment

Performance Analysis of YOLO-based Architectures for Vehicle Detection from Traffic Images in Bangladesh

Evaluation of YOLO Models with Sliced Inference for Small Object Detection

AI-Assisted Verification of Biometric Data Collection

Tea Chrysanthemum Detection under Unstructured Environments Using the TC-YOLO Model