Improving Fire and Smoke Detection with You Only Look Once 11 and Multi-Scale Convolutional Attention DOI Creative Commons
Yuxuan Li,

Lisha Nie,

Fangrong Zhou

et al.

Fire, Journal Year: 2025, Volume and Issue: 8(5), P. 165 - 165

Published: April 22, 2025

Fires pose significant threats to human safety, health, and property. Traditional methods, with their inefficient use of features, struggle meet the demands fire detection. You Only Look Once (YOLO), as an efficient deep learning object detection framework, can rapidly locate identify smoke objects in visual images. However, research utilizing latest YOLO11 for remains sparse, addressing scale variability well practicality models continues be a focus. This study first compares classic YOLO series analyze its advantages tasks. Then, tackle challenges model practicality, we propose Multi-Scale Convolutional Attention (MSCA) mechanism, integrating it into create YOLO11s-MSCA. Experimental results show that outperforms other by balancing accuracy, speed, practicality. The YOLO11s-MSCA performs exceptionally on D-Fire dataset, improving overall accuracy 2.6% recognition 2.8%. demonstrates stronger ability small objects. Although remain handling occluded targets complex backgrounds, exhibits strong robustness generalization capabilities, maintaining performance complicated environments.

Language: Английский

RNN-LSTM: From applications to modeling techniques and beyond—Systematic review DOI Creative Commons
Safwan Mahmood Al-Selwi, Mohd Fadzil Hassan, Said Jadid Abdulkadir

et al.

Journal of King Saud University - Computer and Information Sciences, Journal Year: 2024, Volume and Issue: 36(5), P. 102068 - 102068

Published: May 21, 2024

Long Short-Term Memory (LSTM) is a popular Recurrent Neural Network (RNN) algorithm known for its ability to effectively analyze and process sequential data with long-term dependencies. Despite popularity, the challenge of initializing optimizing RNN-LSTM models persists, often hindering their performance accuracy. This study presents systematic literature review (SLR) using an in-depth four-step approach based on PRISMA methodology, incorporating peer-reviewed articles spanning 2018-2023. It aims address how weight initialization optimization techniques can bolster performance. SLR offers detailed overview across various applications domains, stands out by comprehensively analyzing modeling techniques, datasets, evaluation metrics, programming languages associated networks. The findings this provide roadmap researchers practitioners enhance networks achieve superior results.

Language: Английский

Citations

63
A Comprehensive Survey of Deep Learning Approaches in Image Processing DOI Creative Commons
Μαρία Τρίγκα, Ηλίας Δρίτσας

Sensors, Journal Year: 2025, Volume and Issue: 25(2), P. 531 - 531

Published: Jan. 17, 2025

The integration of deep learning (DL) into image processing has driven transformative advancements, enabling capabilities far beyond the reach traditional methodologies. This survey offers an in-depth exploration DL approaches that have redefined processing, tracing their evolution from early innovations to latest state-of-the-art developments. It also analyzes progression architectural designs and paradigms significantly enhanced ability process interpret complex visual data. Key such as techniques improving model efficiency, generalization, robustness, are examined, showcasing DL's address increasingly sophisticated image-processing tasks across diverse domains. Metrics used for rigorous evaluation discussed, underscoring importance performance assessment in varied application contexts. impact is highlighted through its tackle challenges generate actionable insights. Finally, this identifies potential future directions, including emerging technologies like quantum computing neuromorphic architectures efficiency federated privacy-preserving training. Additionally, it highlights combining with edge explainable artificial intelligence (AI) scalability interpretability challenges. These advancements positioned further extend applications DL, driving innovation processing.

Language: Английский

Citations

1
Comprehensive Performance Evaluation of YOLO11, YOLOv10, YOLOv9 and YOLOv8 on Detecting and Counting Fruitlet in Complex Orchard Environments DOI Creative Commons
Ranjan Sapkota,

Zhichao Meng,

Martin Churuvija

et al.

Published: Oct. 18, 2024

Object detection, specifically fruitlet is a crucial image processing technique in agricultural automation, enabling the accurate identification of fruitlets on orchard trees within images. It vital for early fruit load management and overall crop management, facilitating effective deployment automation robotics to optimize productivity resource use. This study systematically performed an extensive evaluation performances all configurations YOLOv8, YOLOv9, YOLOv10, YOLO11 object detection algorithms terms precision, recall, mean Average Precision at 50% Intersection over Union (mAP@50), computational speeds including pre-processing, inference, post-processing times immature green apple (or fruitlet) commercial orchards. Additionally, this research validated in-field counting using iPhone machine vision sensors 4 different varieties (Scifresh, Scilate, Honeycrisp & Cosmic crisp). investigation total 22 YOLOv10 (5 6 5 YOLO11) revealed that YOLOv9 gelan-base YOLO11s outperforms other YOLOv8 mAP@50 with score 0.935 0.933 respectively. In specifically, Gelan-e achieved highest 0.935, outperforming YOLOv11s's 0.0.933, YOLOv10s’s 0.924, YOLOv8s's 0.924. value among (0.899), YOLO11m best (0.897). comparison inference speeds, YOLO11n demonstrated fastest only 2.4 ms, while speed across were 5.5, 11.5 4.1 ms YOLOv10n, gelan-s YOLOv8n

Language: Английский

Citations

7
The Evolution of Artificial Intelligence in Medical Imaging: From Computer Science to Machine and Deep Learning DOI Open Access
Michele Avanzo,

Joseph Stancanello,

G. Pirrone

et al.

Cancers, Journal Year: 2024, Volume and Issue: 16(21), P. 3702 - 3702

Published: Nov. 1, 2024

Artificial intelligence (AI), the wide spectrum of technologies aiming to give machines or computers ability perform human-like cognitive functions, began in 1940s with first abstract models intelligent machines. Soon after, 1950s and 1960s, machine learning algorithms such as neural networks decision trees ignited significant enthusiasm. More recent advancements include refinement algorithms, development convolutional efficiently analyze images, methods synthesize new images. This renewed enthusiasm was also due increase computational power graphical processing units availability large digital databases be mined by networks. AI soon applied medicine, through expert systems designed support clinician's later for detection, classification, segmentation malignant lesions medical A prospective clinical trial demonstrated non-inferiority alone compared a double reading two radiologists on screening mammography. Natural language processing, recurrent networks, transformers, generative have both improved capabilities making an automated images moved domains, including text analysis electronic health records, image self-labeling, self-reporting. The open-source free libraries, well powerful computing resources, has greatly facilitated adoption deep researchers clinicians. Key concerns surrounding healthcare need trials demonstrate efficacy, perception tools 'black boxes' that require greater interpretability explainability, ethical issues related ensuring fairness trustworthiness systems. Thanks its versatility impressive results, is one most promising resources frontier research applications particular oncological applications.

Language: Английский

Citations

5
Enhanced Colon Cancer Segmentation and Image Synthesis through Advanced Generative Adversarial Networks based-Sine Cosine Algorithm DOI Creative Commons

Alawi Alqushaibi,

Mohd Hilmi Hasan, Said Jadid Abdulkadir

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 105354 - 105369

Published: Jan. 1, 2024

Colorectal cancer (CRC) is a prevalent and life-threatening malignancy, demanding early diagnosis effective treatment for improved patient outcomes. Accurate segmentation of colon in medical images challenging task due to the complexity its morphology limited annotated data availability. This paper presents an efficient approach image synthesis, combining Attention U-Net Pix2Pix Generative Adversarial Network (Pix2Pix-GAN) guided by Sine Cosine Algorithm (SCA) hyperparameter tuning within GAN framework. The utilization SCA plays pivotal role optimizing delicate balance between generator discriminator dynamics, resulting enhanced convergence stability. Our method achieved state-of-the-art results with mean Dice score 0.9514, Intersection over Union 0.9123, F beta 0.9636, similarity index 0.9430 outperforming existing methods. Moreover, Mean Absolute Error reached minimal value 0.01583. proposed shows promise enhancing accuracy robustness which could lead better cancer.

Language: Английский

Citations

4
Overview of deep learning YOLO algorithm DOI

Yaohui Pan,

Gang Wang, Jun Yu

et al.

Published: Jan. 9, 2025

At present, the YOLO algorithm has become an indispensable core real-time object detection technology in aspects such as unmanned driving, face detection, and robot applications, its versions are constantly being updated upgraded. Herein, we deeply analyze evolution process of carefully investigate innovations contributions arising from iterations YOLOv1 to YOLOv5. We make vivid inspiring prospects for future development direction point out feasibility necessity research on algorithm.

Language: Английский

Citations

0
Advancing Fisheries Research and Management with Computer Vision: A Survey of Recent Developments and Pending Challenges DOI Creative Commons
Jesse Eickholt, Jonathan Gregory,

Kavya Vemuri

et al.

Fishes, Journal Year: 2025, Volume and Issue: 10(2), P. 74 - 74

Published: Feb. 12, 2025

The field of computer vision has progressed rapidly over the past ten years, with noticeable improvements in techniques to detect, locate, and classify objects. Concurrent these advances, improved accessibility through machine learning software libraries sparked investigations applications across multiple domains. In areas fisheries research management, efforts have centered on localization fish classification by species, as such tools can estimate health, size, movement populations. To aid interpretation for management tasks, a survey recent literature was conducted. contrast prior reviews, this focuses employed evaluation metrics datasets well challenges associated applying context. Misalignment between commonly used mischaracterizes efficacy emerging tasks. Aqueous, turbid, variable lighted deployment settings further complicate use generalizability reported results. Informed inherent challenges, culling surveillance data, exploratory data collection remote settings, selective passage traps are presented opportunities future research.

Language: Английский

Citations

0
Comparative Performance Evaluation of YOLOv5, YOLOv8, and YOLOv11 for Solar Panel Defect Detection DOI Creative Commons
Rahima Khanam,

Tahreem Asghar,

Muhammad Hussain

et al.

Solar, Journal Year: 2025, Volume and Issue: 5(1), P. 6 - 6

Published: Feb. 21, 2025

The reliable operation of photovoltaic (PV) systems is essential for sustainable energy production, yet their efficiency often compromised by defects such as bird droppings, cracks, and dust accumulation. Automated defect detection critical addressing these challenges in large-scale solar farms, where manual inspections are impractical. This study evaluates three YOLO object models—YOLOv5, YOLOv8, YOLOv11—on a comprehensive dataset to identify panel defects. YOLOv5 achieved the fastest inference time (7.1 ms per image) high precision (94.1%) cracked panels. YOLOv8 excelled recall rare defects, drops (79.2%), while YOLOv11 delivered highest [email protected] (93.4%), demonstrating balanced performance across categories. Despite strong common like dusty panels ([email protected] > 98%), drop posed due imbalances. These results highlight trade-offs between accuracy computational efficiency, providing actionable insights deploying automated enhance PV system reliability scalability.

Language: Английский

Citations

0
Automatic Meniscus Segmentation Using YOLO-Based Deep Learning Models with Ensemble Methods in Knee MRI Images DOI Creative Commons
Mehmet Ali Şimşek, Ahmet Sertbaş, Hadi Sasanı

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(5), P. 2752 - 2752

Published: March 4, 2025

The meniscus is a C-shaped connective tissue with cartilage-like structure in the knee joint. This study proposes an innovative method based on You Only Look Once (YOLO) series models and ensemble methods for segmentation from magnetic resonance imaging (MRI) images to improve performance evaluate generalization capability. In this study, five different were trained, masks created YOLO series. These are combined pixel-based voting, weighted multiple dynamic voting optimized by grid search. Tests conducted internal external sets various metrics. search performed best both test set (DSC: 0.8976 ± 0.0071, PPV: 0.8561 0.0121, Sensitivity: 0.9467 0.0077) 0.9004 0.0064, 0.8876 0.0134, 0.9200 0.0119). proposed offer high accuracy, reliability, capability segmentation.

Language: Английский

Citations

0
Models to Identify Small Brain White Matter Hyperintensity Lesions DOI Creative Commons
Darwin Castillo, María José Rodríguez-Álvarez,

René Samaniego

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(5), P. 2830 - 2830

Published: March 6, 2025

According to the World Health Organization (WHO), peripheral and central neurological disorders affect approximately one billion people worldwide. Ischemic stroke Alzheimer’s Disease other dementias are second fifth leading causes of death, respectively. In this context, detecting classifying brain lesions constitute a critical area research in medical image processing, significantly impacting clinical practice. Traditional lesion detection, segmentation, feature extraction methods time-consuming observer-dependent. sense, machine deep learning applied processing crucial tools for automatically hierarchical features get better accuracy, quick diagnosis, treatment, prognosis diseases. This project aims develop implement models small White Matter hyperintensities (WMH) magnetic resonance images (MRI), specifically concerning ischemic demyelination The were UNet Segmenting Anything model (SAM) while YOLOV8 Detectron2 (based on MaskRCNN) also detect classify lesions. Experimental results show Dice coefficient (DSC) 0.94, 0.50, 0.241, 0.88 segmentation WMH using UNet, SAM, YOLOv8, Detectron2, demonstrated an accuracy 0.94 0.98 lesions, including where often fail. developed give outline classification irregular morphology could aid diagnostics, providing reliable support physicians improving patient outcomes.

Language: Английский

Citations

0