icip2022 paper: sahi benchmark on visdrone and xview datasets using fcos, vfnet and tood detectors

ECCV2018(Challenge-Object Detection in Images)

VisDrone aerial object detection toolkit with 33 models (Torchvision + YOLO), training, evaluation, video inference, benchmarking, and annotation conversion.

Many yolov8 model are trained on the VisDrone dataset.

Simple implement of CenterNet on VisDrone dataset.

YOLO-TLP: detected and classified tiny objects with bounding box dimensions smaller than 15 pixels, outperforming other one-stage detectors. maximum resolution for target observation in real-time applications.

Hybrid RT DETR: Hybrid encoder-decoder network for end-to-end object detection in UAV imagery

Small Object Detection in Dense UAV Imagery using YOLOv8-L with Structured Ablation (EMA, P2 Head, PIoU) and SAHI Evaluation on VisDrone2019.

Official PyTorch code for DroneScan-YOLO (arXiv 2604.13278) : a YOLOv8-based aerial detector featuring Redundancy-Pruned Attention (RPA), Multi-Scale Feature Distillation with a P2 head (MSFD), and a Size-Adaptive Wasserstein-NWD loss. Optimized for sub-32px objects on VisDrone and edge hardware.

dataset or annotation file format conversion

Object Detection on the Visdrone dataset

An end-to-end computer vision system for aerial imagery, implemented and validated on a commercial drone for real-time video inference

Small object detection on VisDrone with YOLOv11s + SAHI

Object detection format converter from VisDrone2019-DET to Yolo.

Smart traffic analysis application.

Reproducible VisDrone experiments for adaptive tile budgeting and scale-aware hybrid inference in UAV small-object detection.

Real-time drone traffic object detection and tracking using YOLOv8m, ByteTrack, OpenCV and VisDrone dataset.

Attention-guided object detection on VisDrone using Grad-CAM++ and YOLO, with metrics and visual outputs.