The MIS Check-Dam Dataset for Object Detection and Instance Segmentation Tasks
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Abstract (arXiv)
Deep learning has led to many recent advances in object detection and instance segmentation, among other computer vision tasks. These advancements have led to wide application of deep learning based methods and related methodologies in object detection tasks for satellite imagery. In this paper, we introduce MIS Check-Dam, a new dataset of check-dams from satellite imagery for building an automated system for the detection and mapping of check-dams, focusing on the importance of irrigation structures used for agriculture. We review some of the most recent object detection and instance segmentation methods and assess their performance on our new dataset. We evaluate several single stage, two-stage and attention based methods under various network configurations and backbone architectures. The dataset and the pre-trained models are available at https://www.cse.iitb.ac.in/gramdrishti/.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 34 of its 45 references that are in the map. First posted on arXiv 30 Nov 2021.
Builds on
34 of 45 references.
References · 34
- 2014Microsoft COCO: Common Objects in Contextcited by 17,608
- 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
- 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
- 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,422
- 2015SSD: Single Shot MultiBox Detectorcited by 3,130
- 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,073
- 2015Fast R-CNNcited by 2,933
- 2018YOLOv3: An Incremental Improvementcited by 2,659
- 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,162
- 2019FCOS: Fully Convolutional One-Stage Object Detectioncited by 1,577
22 more
- 2016YOLO9000: Better, Faster, Strongercited by 1,431
- 2019MMDetection: Open MMLab Detection Toolbox and Benchmarkcited by 1,385
- 2018CornerNet: Detecting Objects as Paired Keypointscited by 763
- 2017Deep Reinforcement Learning that Matterscited by 754
- 2020Sparse R-CNN: End-to-End Object Detection with Learnable Proposalscited by 495
- 2019Hybrid Task Cascade for Instance Segmentationcited by 481
- 2019YOLACT: Real-time Instance Segmentationcited by 443
- 2019Cascade R-CNN: High Quality Object Detection and Instance Segmentationcited by 429
- 2020Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detectioncited by 409
- 2019Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmarkcited by 340
- 2020DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolutioncited by 222
- 2017Drone-based Object Counting by Spatially Regularized Regional Proposal Networkcited by 156
- 2020Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Trainingcited by 103
- 2019InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pastingcited by 95
- 2019FreeAnchor: Learning to Match Anchors for Visual Object Detectioncited by 90
- 2019Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolutioncited by 57
- 2019Prime Sample Attention in Object Detectioncited by 55
- 2020RarePlanes: Synthetic Data Takes Flightcited by 48
- 2020CentripetalNet: Pursuing High-quality Keypoint Pairs for Object Detectioncited by 41
- 2019SpaceNet MVOI: a Multi-View Overhead Imagery Datasetcited by 29
- 2020A novel Region of Interest Extraction Layer for Instance Segmentationcited by 17
- 2021Learning Unsupervised Cross-domain Image-to-Image Translation Using a Shared Discriminatorcited by 4
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