Small Object Detection in Highly Variable Backgrounds

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 detect...

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Bibliographic Details
Published in2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA) pp. 32 - 37
Main Authors Schembri, Michael, Seychell, Dylan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2019
Subjects
Detectors
Feature extraction
Heating systems
machine vision
Object detection
Remote sensing
Signal processing algorithms
Training
unmanned aerial vehicles
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Summary: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.
ISSN:1849-2266
DOI:10.1109/ISPA.2019.8868719