mlsanityData to 14 Oct 2026

Exploring Different Levels of Supervision for Detecting and Localizing Solar Panels on Remote Sensing Imagery

arXiv 2309.10421 ↗First posted 19 Sep 2023cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

This study investigates object presence detection and localization in remote sensing imagery, focusing on solar panel recognition. We explore different levels of supervision, evaluating three models: a fully supervised object detector, a weakly supervised image classifier with CAM-based localization, and a minimally supervised anomaly detector. The classifier excels in binary presence detection (0.79 F1-score), while the object detector (0.72) offers precise localization. The anomaly detector requires more data for viable performance. Fusion of model results shows potential accuracy gains. CAM impacts localization modestly, with GradCAM, GradCAM++, and HiResCAM yielding superior results. Notably, the classifier remains robust with less data, in contrast to the object detector.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 33 references that are in the map. First posted on arXiv 19 Sep 2023.

Builds on

15 of 33 references.

References · 15

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  5. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  6. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  7. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  8. 2017Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networkscited by 939
  9. 2019Object Detection in 20 Years: A Surveycited by 531
  10. 2019Full-Gradient Representation for Neural Network Visualizationcited by 191
  11. 2020Eigen-CAM: Class Activation Map using Principal Componentscited by 189
  12. 2020The elephant in the interpretability room: Why use attention as explanation when we have saliency methods?cited by 122
3 more
  1. 2020Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networkscited by 79
  2. 2021A survey of image labelling for computer vision applicationscited by 18
  3. 2021Looking at the whole picture: constrained unsupervised anomaly segmentationcited by 4

Built on by

Not cited yet.