Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/90066
Title: | Small object detection in highly variable backgrounds |
Authors: | Schembri, Michael Seychell, Dylan |
Keywords: | Computer vision Object-oriented methods (Computer science) Remote sensing Drone aircraft |
Issue Date: | 2019 |
Publisher: | IEEE |
Citation: | Schembri, M., & Seychell, D. (2019). Small object detection in highly variable backgrounds. 11th International Symposium on Image and Signal Processing and Analysis (ISPA), Dubrovnik. 32-37. |
Abstract: | The analysis of imagery from outdoor remote sensing is a technique widely used for surveying and data gathering. This paper studies techniques to be deployed in small object localisation using Convolutional Neural Networks (CNN), with the aim to detect litter in outdoor non-urban imagery. The detection of small objects requires distinguishing features between foreground and background. A litter detection application has to counter high variability in the foreground, as litter is defined as a super-class of common objects, and the high variability found in a rural or coastal backgrounds. Remote sensing imagery of non-urban scenery does not offer high contrasting features, reducing the effect of normal object localisation techniques. |
URI: | https://www.um.edu.mt/library/oar/handle/123456789/90066 |
Appears in Collections: | Scholarly Works - FacICTAI |
Files in This Item:
File | Description | Size | Format | |
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Small_object_detection_in_highly_variable_backgrounds.pdf Restricted Access | 1.62 MB | Adobe PDF | View/Open Request a copy |
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