YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once Series DOI Open Access
Ranjan Sapkota, Rizwan Qureshi, Marco Flores-Calero

et al.

Published: June 24, 2024

This review systematically examines the progression of You Only Look Once (YOLO) object detection algorithms from YOLOv1 to recently unveiled YOLOv10. Employing a reverse chronological analysis, this study advancements introduced by YOLO algorithms, beginning with YOLOv10 and progressing through YOLOv9, YOLOv8, subsequent versions explore each version's contributions enhancing speed, accuracy, computational efficiency in real-time detection. The highlights transformative impact across five critical application areas: automotive safety, healthcare, industrial manufacturing, surveillance, agriculture. By detailing incremental technological that iteration brought, not only chronicles evolution but also discusses challenges limitations observed earlier versions. signifies path towards integrating multimodal, context-aware, General Artificial Intelligence (AGI) systems for next decade, promising significant implications future developments AI-driven applications.

Language: Английский

YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once Series DOI Open Access
Ranjan Sapkota, Rizwan Qureshi, Marco Flores-Calero

et al.

Published: June 20, 2024

This review systematically examines the progression of You Only Look Once (YOLO) object detection algorithms from YOLOv1 to recently unveiled YOLOv10. Employing a reverse chronological analysis, this study advancements introduced by YOLO algorithms, beginning with YOLOv10 and progressing through YOLOv9, YOLOv8, subsequent versions explore each version's contributions enhancing speed, accuracy, computational efficiency in real-time detection. The highlights transformative impact across five critical application areas: automotive safety, healthcare, industrial manufacturing, surveillance, agriculture. By detailing incremental technological that iteration brought, not only chronicles evolution but also discusses challenges limitations observed earlier versions. signifies path towards integrating multimodal, context-aware, General Artificial Intelligence (AGI) systems for next decade, promising significant implications future developments AI-driven applications.

Language: Английский

Citations

13
YOLOGX: an improved forest fire detection algorithm based on YOLOv8 DOI Creative Commons

Caixiong Li,

Yue Du, Xing Zhang

et al.

Frontiers in Environmental Science, Journal Year: 2025, Volume and Issue: 12

Published: Jan. 7, 2025

To tackle issues, including environmental sensitivity, inadequate fire source recognition, and inefficient feature extraction in existing forest detection algorithms, we developed a high-precision algorithm, YOLOGX. YOLOGX integrates three pivotal technologies: First, the GD mechanism fuses extracts features from multi-scale information, significantly enhancing capability for targets of varying sizes. Second, SE-ResNeXt module is integrated into head, optimizing capability, reducing number parameters, improving accuracy efficiency. Finally, proposed Focal-SIoU loss function replaces original function, effectively directional errors by combining angle, distance, shape, IoU losses, thus model training process. was evaluated on D-Fire dataset, achieving [email protected] 80.92% speed 115 FPS, surpassing most classical algorithms specialized models. These enhancements establish as robust efficient solution detection, providing significant improvements reliability.

Language: Английский

Citations

1
YOLO-ESIDE: fire hydrant detection under fire environment DOI
Hua Xu

Signal Image and Video Processing, Journal Year: 2025, Volume and Issue: 19(3)

Published: Jan. 17, 2025

Language: Английский

Citations

0
Advanced Object Detection for Maritime Fire Safety DOI Creative Commons

Fazliddin Makhmudov,

Sabina Umirzakova,

Alpamis Kutlimuratov

et al.

Fire, Journal Year: 2024, Volume and Issue: 7(12), P. 430 - 430

Published: Nov. 25, 2024

In this study, we propose an advanced object detection model for fire and smoke in maritime environments, leveraging the DETR (Detection with Transformers) framework. To address specific challenges of shipboard detection, such as varying lighting conditions, occlusions, complex structure ships, enhance baseline by integrating EfficientNet-B0 backbone. This modification aims to improve accuracy while maintaining computational efficiency. We utilize a custom dataset images captured from diverse incorporating range data augmentation techniques increase robustness. The proposed is evaluated against YOLOv5 variants, showing significant improvements Average Precision (AP), especially detecting small medium-sized objects. Our achieves superior AP score 38.7 outperforms alternative models across multiple IoU thresholds (AP50, AP75), particularly scenarios requiring high precision occluded experimental results highlight model’s efficacy early demonstrating its potential deployment real-time safety monitoring systems. These findings provide foundation future research aimed at enhancing challenging environments.

Language: Английский

Citations

3
Human Remains Detection in Natural Disasters using YOLO: A Deep Learning Approach DOI Open Access

Jyotsna Rani Thota,

Anuradha Padala

Engineering Technology & Applied Science Research, Journal Year: 2024, Volume and Issue: 14(6), P. 17678 - 17682

Published: Dec. 2, 2024

Natural catastrophes are defined as events whose precise location and timing unexpected. disasters can cause property damage death. The NDRF has to coordinate rapid evacuation help victims of natural minimize their losses. In reality, the process is rather challenging. journey begins with tackling challenging terrain ends equipment limitations. Most studies focus on classifying various types disasters, estimating amount incurred during a disaster, identifying in post-disaster situations. Many use image processing locate vulnerable locations. This study aims establish system for human bodies after assist teams volunteers find hard-to-reach areas. You Only Look Once (YOLO) method used conjunction artificial intelligence's computer vision algorithms Python programming language effectively detect an accuracy 96%.

Language: Английский

Citations

3
YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once Series DOI Open Access
Ranjan Sapkota, Rizwan Qureshi, Marco Flores-Calero

et al.

Published: June 24, 2024

This review systematically examines the progression of You Only Look Once (YOLO) object detection algorithms from YOLOv1 to recently unveiled YOLOv10. Employing a reverse chronological analysis, this study advancements introduced by YOLO algorithms, beginning with YOLOv10 and progressing through YOLOv9, YOLOv8, subsequent versions explore each version's contributions enhancing speed, accuracy, computational efficiency in real-time detection. The highlights transformative impact across five critical application areas: automotive safety, healthcare, industrial manufacturing, surveillance, agriculture. By detailing incremental technological that iteration brought, not only chronicles evolution but also discusses challenges limitations observed earlier versions. signifies path towards integrating multimodal, context-aware, General Artificial Intelligence (AGI) systems for next decade, promising significant implications future developments AI-driven applications.

Language: Английский

Citations

0