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Talks and Poster Presentations (with Proceedings-Entry):

N. Djukic, W. Kropatsch, M. Vincze:
"The Difficulties of Detecting Deformable Objects Using Deep Neural Networks";
Talk: Computer Vision and Robotics Workshop 2020, Österreich, TU Graz; 2020-04-16 - 2020-04-17; in: "Proceedings of the Joint Austrian Computer Vision and Robotics Workshop 2020", (2020), ISBN: 978-3-85125-752-6; 6 pages.



English abstract:
Object detectors based on deep neuralnetworks have revolutionized the way we look forobjects in an image, outperforming traditional im-age processing techniques. These detectors are of-ten trained on huge datasets of labelled images andare used to detect objects of different classes. We ex-plore how they perform at detecting custom objectsand show how shape and deformability of an objectaffect the detection performance. We propose an au-tomated method for synthesizing the training imagesand target the real-time scenario using YOLOv3 asthe baseline for object detection. We show that rigidobjects have a high chance of being detected withan AP (average precision) of 87.38%. Slightly de-formable objects like scissors and headphones showa drop in detection performance with precision aver-aging at 49.54%. Highly deformable objects like achain or earphones show an even further drop in APto 26.58%.

Keywords:
deep neural networks, detectors, image processing


"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.3217/978-3-85125-752-6-30


Created from the Publication Database of the Vienna University of Technology.