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Multi-Camera Machine Vision for Detecting and Analyzing Vehicle–Pedestrian Conflicts at Signalized Intersections: Deep Neural-Based Pose Estimation Algorithms

Ahmed Mohamed, Mohamed M. Ahmed • Applied Sciences (2025)

Over the past decade, researchers have advanced traffic monitoring using surveillance cameras, unmanned aerial vehicles (UAVs), loop detectors, LiDAR, microwave sensors, and sensor fusion. These technologies effectively detect and track veh...

DOAJ Open Access (DOAJ) DOI: 10.3390/app151910413

DETECTION AND RECOGNITION OF ROAD SIGNS USING YOLOv5

Haitam Ettazi, Najat Rafalia, Jaafar Abouchabaka • Proceedings on Engineering Sciences (2023)

In the field of deep learning, a convolutional neural network is a class of artificial neural networks that became dominant in various computer vision tasks, which is widely used to solve complex problems in various areas, including driver ...

DOAJ Open Access (DOAJ) DOI: 10.24874/PES05.03.017

A comprehensive survey on RGB-D-based human action recognition: algorithms, datasets, and popular applications

Yumin Zhang, Yanyong Wang • EURASIP Journal on Image and Video Processing (2025)

Abstract Due to the rapid advances in computer vision and deep learning, human action recognition has become one of the most important representative tasks for video understanding. Especially for human action recognition based on RGB-D data...

DOAJ Open Access (DOAJ) DOI: 10.1186/s13640-025-00677-0

Identification of camouflage military individuals with deep learning approaches DFAN and SINETV2

Ali Haider, Ghulam Muhammad, Talha Ahmed Khan, Kushsairy Kadir et al. • Scientific Reports (2025)

Abstract Camouflaged object detection particularly is considered as challenging and crucial because these objects are designed to either mimic their environment or be completely hidden within it. The goal of camouflage patterns utilization ...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-025-18886-y

Segmentation of muscle tissue in computed tomography images at the level of the L3 vertebra

A. R. Teplyakova, R. V. Shershnev, S. O. Starkov, V. A. Kukarskaya • Научно-технический вестник информационных технологий, механики и оптики (2024)

With the increasing routine workload on radiologists associated with the need to analyze large numbers of images, there is a need to automate part of the analysis process. Sarcopenia is a condition in which there is a loss of muscle mass. T...

DOAJ Open Access (DOAJ) DOI: 10.17586/2226-1494-2024-24-1-124-132

DIAGNOSTICS OF RUSTS AND SPOTTS OF WHEAT USING COMPUTER VISION METHODS

Irina V. Arinicheva, Igor V. Arinichev, Galina V. Volkova, Sergey V. Polyanskikh • Siberian Journal of Life Sciences and Agriculture (2022)

Purpose. To investigate the possibility of diagnosing yellow and brown rust (Puccinia striiformis f. sp. tritici West. Puccinia triticina f. sp. tritici Erikss), yellow spot (Pyrenophora tritici-repentis (Died.) Drechsler) on wheat leaves f...

DOAJ Open Access (DOAJ) DOI: 10.12731/2658-6649-2022-14-1-248-261

Computer Vision System for Automatic Counting of Planting Microsites Using UAV Imagery

Wassim Bouachir, Koffi Eddy Ihou, Houssem-Eddine Gueziri, Nizar Bouguila et al. • IEEE Access (2019)

Mechanical site preparation by mounding is often used by the forest industry to provide optimal growth conditions for tree seedlings. Prior to planting, an essential step consists in estimating the number of mounds at each planting block, w...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2019.2923765

Geo-Temporal Selective Approach for Dynamic Depth Estimation in Outdoor Object Detection and Distance Measurement

Muhammad Faseeh, Misbah Bibi, Murad Ali Khan, Atif Rizwan et al. • IEEE Access (2024)

Accurate depth information is crucial for various computer vision applications such as augmented reality, 3D modeling, and autonomous vehicles. Recent advancements have significantly improved both supervised and self-supervised methods for ...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2024.3485089

Bridging the performance–efficiency gap: a resource-efficient hybrid DL-ML framework for jackfruit disease detection

Rittik Chandra Das Turjy, Fahmid Al Farid, Fahmid Al Farid, Md. Hasan Imam Bijoy et al. • Frontiers in Artificial Intelligence (2026)

Complex environment variability and limited computational resources in agricultural settings create challenges in automated detection in tropical regions, where jackfruit diseases pose major threats to harvest productivity and food security...

DOAJ Open Access (DOAJ) DOI: 10.3389/frai.2026.1884567

Using cascade CNN-LSTM-FCNs to identify AI-altered video based on eye state sequence

Muhammad Salihin Saealal, Mohd Zamri Ibrahim, David. J. Mulvaney, Mohd Ibrahim Shapiai et al. • PLoS ONE (2022)

Deep learning is notably successful in data analysis, computer vision, and human control. Nevertheless, this approach has inevitably allowed the development of DeepFake video sequences and images that could be altered so that the changes ar...

DOAJ Open Access (DOAJ)

Fruit freshness detection based on multi-task convolutional neural network

Yinsheng Zhang, Xudong Yang, Yongbo Cheng, Xiaojun Wu et al. • Current Research in Food Science (2024)

Background: Fruit freshness detection by computer vision is essential for many agricultural applications, e.g., automatic harvesting and supply chain monitoring. This paper proposes to use the multi-task learning (MTL) paradigm to build a d...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.crfs.2024.100733

Performance Benchmarking: Pre-trained Models and Custom Convolutional Neural Networks in Deep Learning

Sheemona Joseph C., S. Ganesh, S. Kannadhasan, K. Selvipriya • Journal of Informatics and Web Engineering (2025)

Recent advances in computer vision and deep learning, particularly Convolutional Neural Networks (CNNs), have significantly increased road safety. CNNs were used in this work to automatically detect and categorise traffic signs—a crucial ta...

DOAJ Open Access (DOAJ) DOI: 10.33093/jiwe.2025.4.2.14

Improved U-Net Model for Road Crack Detection Based on Residual and Attention Mechanism

YU Haiyang, JING Peng, ZHANG Wentao, XIE Saifei, HUA Zhihua, SONG Caoyuan • Jisuanji gongcheng (2023)

Road cracks are an important part of road safety detection,and with the development of deep learning and computer vision,methods for extracting crack information in road images using deep learning methods are maturing.Existing deep learning...

DOAJ Open Access (DOAJ) DOI: 10.19678/j.issn.1000-3428.0064952

TransCat: a hybrid CNN-transformer network with KAN for medical image segmentation

Jin Wang, Zhenghua Yang, Dongming Zhou, Dongming Zhou et al. • Frontiers in Bioinformatics (2026)

Medical image segmentation is a fundamental task in computer vision and plays an important role in clinical diagnosis and treatment planning. Existing methods are mainly built on either convolutional neural networks (CNNs) or Transformer-ba...

DOAJ Open Access (DOAJ) DOI: 10.3389/fbinf.2026.1849059

Deep Neural Networks and Kernel Density Estimation for Detecting Human Activity Patterns from Geo-Tagged Images: A Case Study of Birdwatching on Flickr

Caglar Koylu, Chang Zhao, Wei Shao • ISPRS International Journal of Geo-Information (2019)

Thanks to recent advances in high-performance computing and deep learning, computer vision algorithms coupled with spatial analysis methods provide a unique opportunity for extracting human activity patterns from geo-tagged social media ima...

DOAJ Open Access (DOAJ) DOI: 10.3390/ijgi8010045