arXiv:2609.13647v1 Announce Type: new
Abstract: The rapid development of unmanned aerial vehicle (UAV) technology has made aerial-image object detection increasingly important for natural-resource mo...
By Hao Wang
This paper provides a detailed overview of the Ultralytics YOLO family from YOLOv5 to YOLO27, highlighting key architectural changes, benchmarking results, and deployment considerations. It discusses the evolution of each version—YOLO27’s scale‑adaptive dual architecture, YOLO26’s loss and optimization refinements, YOLO11’s efficiency focus, YOLOv8’s anchor‑free detection, and YOLOv5’s modular ecosystem—alongside performance metrics on COCO and latency on TensorRT. The review also surveys applications in robotics, agriculture, surveillance, and manufacturing, and outlines future challenges such as dense scene handling, CNN‑Transformer integration, and hardware‑aware optimization.
By Ranjan Sapkota, Manoj Karkee
The article presents a comparative study of convolutional neural network (CNN) architectures for classifying COVID-19 from healthy lung images using CT and X‑ray scans. Multiple pre‑trained models—including VGG, DenseNet, ResNet, MobileNet, Xception, Inception, EfficientNet, and NasNet—were evaluated on two X‑ray and two CT datasets. ResNet and VGG achieved the highest accuracies, ranging from 95% to 98%, outperforming previous reports in the literature.
By Sarmad Khan, Basim Azam, Arslan Shaukat
arXiv:2607. 12245v1 Announce Type: cross Abstract: Few-shot industrial defect detection remains difficult for standard supervised detectors, which achieve poor performance on boundary-dominated industrial defects.
By Jiaqi Kuang
The study evaluates the performance of the DINOv2 visual representation for classifying Trachomatous Inflammation-Follicular (TF) versus normal conjunctival images. Using 1,546 images processed by the OPTED pipeline, the authors compare six pretrained backbones and then test four lightweight adaptation methods on DINOv2 ViT-B/14. The best results—91.66% accuracy, 90.69% macro‑F1, and 96.06% AUC—were achieved with DINOv2 plus Efficient Channel Attention (ECA) and a focal‑plus‑center loss, though ECA’s benefit varied with the loss function.
By Kibrom Gebremedhin, Hadush Hailu, Bruk Gebregziabher, Yordanos Hailu
arXiv:2606. 06536v1 Announce Type: cross Abstract: Automated defect detection in high-voltage transmission-line insulators remains challenging due to severe class imbalance, large scale variation, and the small spatial extent of defect instances in Unmanned Aerial Vehicle (UAV) imagery.
By Malak Allam, Khaled Shaban, Ali Hamdi