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Application of Transfer Learning for Object Detection on Manually Collected Data

Mohamed, Elhassan and Sirlantzis, Konstantinos and Howells, Gareth (2019) Application of Transfer Learning for Object Detection on Manually Collected Data. In: Intelligent Systems and Applications volume 1. Advances in Intelligent Systems and Computing book series, Intel . Springer, pp. 913-931. ISBN 978-3-030-29515-8. E-ISBN 978-3-030-29516-5. (doi:10.1007/978-3-030-29516-5_69) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:74066)

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This paper investigates the usage of pre-trained deep learning neural networks for object detection on a manually collected dataset for real-life indoor objects. Availability of object-specific datasets is a great challenge and the unavoidable task of collecting, processing and annotating ground truth data is laborious and time-consuming. In this paper, two famous models (AlexNet and Vgg16) have been evaluated as feature extractors in a Faster R-CNN network. Network models have been trained end-to-end on the collected dataset. The study highlights the poor performance of state of art systems when dealing with small size objects. Modifying the detector design by redesigning systems’ anchor boxes might help to tackle this problem. Detector results on the proposed dataset have been collected and compared. In addition, limitations and future work have been discussed.

Item Type: Book section
DOI/Identification number: 10.1007/978-3-030-29516-5_69
Uncontrolled keywords: Object Detection, Deep Learning, Assistive Technology
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Gareth Howells
Date Deposited: 22 May 2019 14:55 UTC
Last Modified: 16 Feb 2021 14:04 UTC
Resource URI: (The current URI for this page, for reference purposes)

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