There are 12 repositories under small-object-detection topic.
🕶 A curated list of Tiny Object Detection papers and related resources.
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icip2022 paper: sahi benchmark on visdrone and xview datasets using fcos, vfnet and tood detectors
The official implementation for ICCV'23 paper "Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning"
This is the repository with the baseline code for the "Small Object Detection Challenge for Spotting Birds" at MVA2023.
[BMVC-20] Official PyTorch implementation of PPDet.
Code for paper "Detection of Flying Honeybees in UAV Videos"
Official code library for SODA: A Large-scale Benchmark for Small Object Detection.
Python library for YOLOv8 and YOLOv9 small object detection and instance segmentation
Official implementation of "CST-YOLO: A Novel Method for Blood Cell Detection Based on Improved YOLOv7 and CNN-Swin Transformer".
Drone / Unmanned Aerial Vehicle (UAV) Detection is a very safety critical project. It takes in Infrared (IR) video streams and detects drones in it with high accuracy.
2023年西交利物浦大学动云科技GMaster战队雷达yolo小目标检测
Annotations for the Aircraft Context Dataset
An official code of Densely-packed Object Detection via Hard Negative-Aware Anchor Attention in WACV2022
[ACCV 2022] AirBirds: A Large-scale Dataset for Bird Strike Prevention in Real-world Airports
Implementation of 'Attention-guided Feature Fusion for Small Object Detection'
Official pytorch implementation for FEN (Feature Enhancement Network)
[ACCV 2022] AirBirds: A Large-scale Dataset for Bird Strike Prevention in Real-world Airports
This repository contains notebooks for training and testing yolov5/X and also contains visualization code.
The SODA Dataset is a computer vision dataset containing aerial imagery of small objects captured at different altitudes. The dataset contains 829 images and 6719 object annotations.
Code of 'F-YOLO: Delving into Fuzzy YOLO for Improved Traffic Object Detection'
Official implementation of "Automated Non-Invasive Analysis of Motile Sperms Using Sperm Feature-Correlated Network".
System for detecting product defects from the production line based on digital image processing using neural networks