Papers with Code 아카이브

Object Detection In Aerial Images

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평가표: 8
보존된 평가 기록: 89

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/Anymake/DRN_CVPR2020

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Dynamic Refinement Network for Oriented and Densely Packed Object Detection

https://github.com/Chasel-Tsui/mmdet-aitod

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Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark

https://github.com/Cli98/DMNet

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Density Map Guided Object Detection in Aerial Images

https://github.com/DetectionTeamUCAS/R2CNN-Plus-Plus_Tensorflow

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SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects

https://github.com/Fengxiang23/SelectiveMAE

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Scaling Efficient Masked Image Modeling on Large Remote Sensing Dataset

https://github.com/HVision-NKU/Strip-R-CNN

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Strip R-CNN: Large Strip Convolution for Remote Sensing Object Detection

https://github.com/Jittor/JDet

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The KFIoU Loss for Rotated Object Detection

https://github.com/Lans1ng/SFOD-RS

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CLIP-Guided Source-Free Object Detection in Aerial Images

https://github.com/LeapLabTHU/ARC

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Adaptive Rotated Convolution for Rotated Object Detection

https://github.com/LiWentomng/OrientedRepPoints

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Oriented RepPoints for Aerial Object Detection

https://github.com/PaddlePaddle/Paddle

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PP-YOLOE-R: An Efficient Anchor-Free Rotated Object Detector

https://github.com/PaddlePaddle/PaddleDetection

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PP-YOLOE-R: An Efficient Anchor-Free Rotated Object Detector

https://github.com/SIAnalytics/RHINO

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Hausdorff Distance Matching with Adaptive Query Denoising for Rotated Detection Transformer

https://github.com/SJTU-Det/R3Det_Tensorflow

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R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object

https://github.com/SJTU-Thinklab-Det/DOTA-DOAI

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SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing

https://github.com/SJTU-Thinklab-Det/OHDet_Tensorflow

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On the Arbitrary-Oriented Object Detection: Classification based Approaches Revisited

https://github.com/SJTU-Thinklab-Det/r3det-on-mmdetection

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R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object

https://github.com/Shank2358/GGHL

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A General Gaussian Heatmap Label Assignment for Arbitrary-Oriented Object Detection

https://github.com/Thinklab-SJTU/CSL_RetinaNet_Tensorflow

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On the Arbitrary-Oriented Object Detection: Classification based Approaches Revisited

https://github.com/Thinklab-SJTU/DCL_RetinaNet_Tensorflow

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Dense Label Encoding for Boundary Discontinuity Free Rotation Detection

https://github.com/Thinklab-SJTU/R3Det_Tensorflow

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R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object

https://github.com/Thinklab-SJTU/S2TLD

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SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing

https://github.com/WeiZongqi/CG-Net

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Learning Calibrated-Guidance for Object Detection in Aerial Images

https://github.com/ZhuHaoranEIS/DN-TOD

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Robust Tiny Object Detection in Aerial Images amidst Label Noise

https://github.com/Zifei-Zhao/OBBDetection

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ABFL: Angular Boundary Discontinuity Free Loss for Arbitrary Oriented Object Detection in Aerial Images

https://github.com/akhilpm/dronessod

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Density Crop-guided Semi-supervised Object Detection in Aerial Images

https://github.com/clobotics/piou

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PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments

https://github.com/csuhan/ReDet

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ReDet: A Rotation-equivariant Detector for Aerial Object Detection

https://github.com/csuhan/s2anet

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Align Deep Features for Oriented Object Detection

https://github.com/dawn-ech/yolc

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YOLC: You Only Look Clusters for Tiny Object Detection in Aerial Images

https://github.com/dingjiansw101/AerialDetection

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Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges

https://github.com/duzw9311/cfpt

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Cross-Layer Feature Pyramid Transformer for Small Object Detection in Aerial Images

https://github.com/fiveai/MoCaE

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MoCaE: Mixture of Calibrated Experts Significantly Improves Object Detection

https://github.com/hehaodong530/dota-c

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On the Robustness of Object Detection Models on Aerial Images

https://github.com/jbwang1997/OBBDetection

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Oriented R-CNN for Object Detection

https://github.com/lizzy8587/castdet

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Toward Open Vocabulary Aerial Object Detection with CLIP-Activated Student-Teacher Learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.