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Rain Drop Image Data Set for Guo 2018 study - still image set Open Access

The presence of raindrop induced image distortion has a significant negative impact on the performance of a wide range of all-weather visual sensing applications including within the increasingly important contexts of visual surveillance and vehicle autonomy. A key part of this problem is robust raindrop detection such that the potential for performance degradation in effected image regions can be identified. Here we address the problem of raindrop detection in colour video imagery by considering three varying region proposal approaches with secondary classification via a number of novel convolutional neural network architecture variants. This is verified over an extensive dataset with in-frame raindrop annotation to achieve maximal 0.95 detection accuracy with minimal false positives compared to prior work. Our approach is evaluated under a range of environmental conditions typical of all-weather automotive visual sensing applications. | Cited in: n The Impact Of Varying Region Proposal Strategies For Raindrop Detection And Classification Using Convolutional Neural Networks (T. Guo, S. Akcay, P. Adey, T.P. Breckon), In Proc. International Conference on Image Processing, IEEE, pp. 3413-3417, 2018. | This collection contains supporting materials in the form of the image data used in the study.

Descriptions

Resource type
Dataset
Contributors
Creator: Guo, Tiancheng 1
Data collector: Guo, Tiancheng 1
Data curator: Guo, Tiancheng 1
Contact person: Breckon, Toby 1
Data curator: Breckon, Toby 1
Editor: Breckon, Toby 1
1 Durham University, UK
Funder
Research methods
Other description
Keyword
Computer vision
Raindrop detection
Rain detection
Rain removal
Rain noise removal
Rain interference
Scene context
Raindrop saliency
Rain classification
Convolutional neural network (CNN)
Deep learning
Subject
Computer vision
Raindrops
Rain and rainfall
Neural networks (Computer science)
Computer Science
Engineering
Location
Language
Cited in
Identifier
ark:/32150/r2jh343s319
doi:10.15128/r2jh343s319
Rights
Creative Commons Attribution 4.0 International (CC BY)

Publisher
Durham University
Date Created

File Details

Depositor
T. Breckon
Date Uploaded
Date Modified
26 November 2018, 13:11:42
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Related Files
guo-raindrop-detection-pretrained-model-2018.zip
Characterization
File format: zip (ZIP Format)
Mime type: application/zip
File size: 42353525
Last modified: 2018:11:25 16:02:27+00:00
Filename: guo-raindrop-detection-dataset-2018.zip
Original checksum: 4575a5abd4431051a2272dcd2731c63f
Activity of users you follow
User Activity Date
User T. Breckon has updated Rain Drop Image Data Set for Guo 2018 study - still image set 20 days ago
User N. Syrotiuk has updated Rain Drop Image Data Set for Guo 2018 study - still image set 21 days ago
User N. Syrotiuk has updated Rain Drop Image Data Set for Guo 2018 study - still image set 21 days ago
User T. Breckon has updated Rain Drop Image Data Set for Guo 2018 study - still image set 21 days ago
User T. Breckon has deposited guo-raindrop-detection-dataset-2018.zip 21 days ago