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Object Detection In Aerial Images
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Object Detection in Aerial Images is the task of detecting objects from aerial images.
<span style="color:grey; opacity: 0.6">( Image credit: DOTA: A Large-Scale Dataset for Object Detection in Aerial Images )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/lwcver/lwganet
공식 코드LWGANet: A Lightweight Group Attention Backbone for Remote Sensing Visual Tasks
https://github.com/ming71/CFC-Net
공식 코드https://github.com/ming71/DAL
공식 코드Dynamic Anchor Learning for Arbitrary-Oriented Object Detection
https://github.com/ming71/RIDet
공식 코드Optimization for Arbitrary-Oriented Object Detection via Representation Invariance Loss
https://github.com/nalemadi/VME_CDSI_dataset_benchmark
공식 코드VME: A Satellite Imagery Dataset and Benchmark for Detecting Vehicles in the Middle East and Beyond
https://github.com/open-mmlab/mmdetection/tree/3.x/configs/rtmdet
공식 코드RTMDet: An Empirical Study of Designing Real-Time Object Detectors
https://github.com/phucnda/ha-rdet
공식 코드HA-RDet: Hybrid Anchor Rotation Detector for Oriented Object Detection
https://github.com/qjadud1994/TricubeNet
공식 코드TricubeNet: 2D Kernel-Based Object Representation for Weakly-Occluded Oriented Object Detection
https://github.com/qxgeng/dea-net
공식 코드Anchor Retouching via Model Interaction for Robust Object Detection in Aerial Images
https://github.com/ssyc123/gsnet
공식 코드Learning to Reduce Information Bottleneck for Object Detection in Aerial Images
https://github.com/toelt-llc/FlightScope_Bench
공식 코드https://github.com/vitae-transformer/mtp
공식 코드MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining
https://github.com/vitae-transformer/remote-sensing-rvsa
공식 코드Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model
https://github.com/vitae-transformer/rsp
공식 코드https://github.com/vitae-transformer/vitae-transformer-remote-sensing
공식 코드https://github.com/yangxue0827/RotationDetection
공식 코드R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object
https://github.com/yijingru/BBAVectors-Oriented-Object-Detection
공식 코드Oriented Object Detection in Aerial Images with Box Boundary-Aware Vectors
https://github.com/yuhongtian17/spatial-transform-decoupling
공식 코드https://github.com/yxb-nku/strip-r-cnn
공식 코드Strip R-CNN: Large Strip Convolution for Remote Sensing Object Detection
https://github.com/zcablii/Large-Selective-Kernel-Network
공식 코드Large Selective Kernel Network for Remote Sensing Object Detection
https://github.com/zcablii/lsknet
공식 코드LSKNet: A Foundation Lightweight Backbone for Remote Sensing
https://github.com/zf020114/dardet
공식 코드DARDet: A Dense Anchor-free Rotated Object Detector in Aerial Images
https://github.com/zhuhaoraneis/orthogonal-fgod
공식 코드Enhancing Fine-grained Object Detection in Aerial Images via Orthogonal Mapping
https://github.com/A511-1103/Express_detection
Dense Label Encoding for Boundary Discontinuity Free Rotation Detection
https://github.com/CAPTAIN-WHU/DOTA_devkit
DOTA: A Large-scale Dataset for Object Detection in Aerial Images
https://github.com/Capino512/pytorch-rotation-decoupled-detector
https://github.com/CycloneBoy/PPDetectionPytorch
RTMDet: An Empirical Study of Designing Real-Time Object Detectors
https://github.com/DetectionTeamUCAS/RetinaNet_Tensorflow_Rotation
SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects
https://github.com/MS-P3/code7/tree/main/vitdet
Aerial Image Object Detection With Vision Transformer Detector (ViTDet)
https://github.com/MortenTabaka/Semantic-segmentation-of-LandCover.ai-dataset
https://github.com/Mrqianduoduo/RSDet-8P-4R
https://github.com/NovasMax/R3Det-Refined-Single-Stage-Detector-with-Feature-Refinement-for-RO
SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects
https://github.com/PaddlePaddle/PaddleYOLO
RTMDet: An Empirical Study of Designing Real-Time Object Detectors
https://github.com/RangiLyu/mmdetection_test
RTMDet: An Empirical Study of Designing Real-Time Object Detectors
https://github.com/SDL-GuoZonghao/BeyondBoundingBox
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