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Showing 14 of 74 academic articles

Systemic Review and Meta-Analysis: The Application of AI-Powered Drone Technology with Computer Vision and Deep Learning Networks in Waste Management

Tyrone Bright, Sarp Adali, Cristina Trois • Drones (2025)

As the generation of Municipal Solid Waste (MSW) has exponentially increased, this poses a challenge for waste managers, such as municipalities, to effectively control waste streams. If waste streams are not managed correctly, they negative...

DOAJ Open Access (DOAJ) DOI: 10.3390/drones9080550

Combining the YOLOv4 Deep Learning Model with UAV Imagery Processing Technology in the Extraction and Quantization of Cracks in Bridges

Szu-Pyng Kao, Yung-Chen Chang, Feng-Liang Wang • Sensors (2023)

Bridges are often at risk due to the effects of natural disasters, such as earthquakes and typhoons. Bridge inspection assessments normally focus on cracks. However, numerous concrete structures with cracked surfaces are highly elevated or ...

DOAJ Open Access (DOAJ) DOI: 10.3390/s23052572

Research on high-precision recognition model for multi-scene asphalt pavement distresses based on deep learning

Sheng Zhang, Zhenghao Bei, Tonghua Ling, Qianqian Chen et al. • Scientific Reports (2024)

Abstract Accurate detection of asphalt pavement distress is crucial for road maintenance and traffic safety. However, traditional convolutional neural networks usually struggle with this task due to the varied distress patterns and complex ...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-024-77173-4

Development of an automated two pronuclei detection system on time‐lapse embryo images using deep learning techniques

Noritaka Fukunaga, Sho Sanami, Hiroya Kitasaka, Yuji Tsuzuki et al. • Reproductive Medicine and Biology (2020)

Abstract Purpose To establish an automated pronuclei determination system by analysis using deep learning technology which is able to effectively learn with limited amount of supervised data. Methods An algorithm was developed by explicitly...

DOAJ Open Access (DOAJ) DOI: 10.1002/rmb2.12331

A deep learning model for the localization and extraction of brain tumors from MR images using YOLOv7 and grab cut algorithm

Srigiri Krishnapriya, Yepuganti Karuna • Frontiers in Oncology (2024)

IntroductionBrain tumors are a common disease that affects millions of people worldwide. Considering the severity of brain tumors (BT), it is important to diagnose the disease in its early stages. With advancements in the diagnostic process...

DOAJ Open Access (DOAJ) DOI: 10.3389/fonc.2024.1347363

Online exam cheating detection and blockchain trusted deposit based on YOLOv12

Haoliang Wang, Zarina Shukur, Khairul Akram Zainol Ariffin, Renhao Xiao et al. • Scientific Reports (2025)

Abstract In recent years, online examinations have been widely adopted because of their flexibility, but the covert and diverse nature of cheating behaviour poses a serious challenge to the fairness and integrity of examinations. Existing a...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-025-18412-0

A review of optimization strategies for deep and machine learning in diabetic macular edema

A. M. Mutawa, Khalid Sabti, Bibin Shalini Sundaram Thankaleela, Seemant Raizada • Frontiers in Artificial Intelligence (2026)

Diabetic macular edema (DME) is a primary contributor to visual impairment in diabetic patients, necessitating precise and prompt analysis for optimal treatment. Recent breakthroughs in deep learning (DL) and machine learning (ML) have yiel...

DOAJ Open Access (DOAJ) DOI: 10.3389/frai.2026.1684752

Computer vision and AI-based cell phone usage detection in restricted zones of manufacturing industries

Uttam U. Deshpande, Supriya Shanbhag, Ramesh Koti, Ameet Chate et al. • Frontiers in Computer Science (2025)

Phone calls are strictly forbidden in certain locations due to the potential security threats. Mobile phones’ growing capabilities have also increased the risk of their misuse in places that are restricted, like manufacturing plants. Unauth...

DOAJ Open Access (DOAJ) DOI: 10.3389/fcomp.2025.1535775

InsDD-YOLO: Detection of Transmission Line Insulator Damage Based on the Improved YOLOv13 Model

Phat T. Nguyen, Duy C. Huynh, Loc D. Ho, Ha T. Tran et al. • IEEE Access (2025)

Amidst the rapid global expansion of smart grids, ensuring the safety and reliability of power transmission systems has become paramount. Insulators are critical components of high-voltage transmission lines, providing both electrical insul...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2025.3648259