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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

CG-DINO: Improved Network for Small Object Detection in Uncrewed Aerial Vehicle Imagery Based on DINO

Jian Liu, Pei Niu, Yihui Jiang, Zilong Zhao et al. • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2026)

Object detection in uncrewed aerial vehicle (UAV) acquired images remains a challenging task due to extreme scale variation, dense object layouts, and complex backgrounds, etc. Although convolutional neural networks (CNNs), transformers, an...

DOAJ Open Access (DOAJ) DOI: 10.1109/JSTARS.2026.3729713

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

Comparative architectural evaluation of CNN models with alternative classifiers for UAV-based detection of damaged green zones

Nodir Rakhimov, Jura Kuvandikov, Ma'ruf Tojiyev, Dilmurod Khasanov et al. • Frontiers in Artificial Intelligence (2026)

Unmanned aerial vehicle imagery provides a practical and high-resolution data source for monitoring the spatial condition of urban and peri-urban green zones. However, the reliable detection of damaged, stressed, or declining vegetation are...

DOAJ Open Access (DOAJ) DOI: 10.3389/frai.2026.1910842