In this paper we present an object detection system based on YOLOv2 and transfer learning which is compared to Keras faster RCNN. We evaluate our systems using two infrared datasets: a small dataset of low resolution thermal images and a large dataset of high resolution thermal images both containing the object classes; people and land vehicles. Both detectors are trained on the large dataset of high resolution thermal images and tested using the low resolution images. Fine tuning on the small dataset is implemented to increase the accuracy of the detectors. This research will be of great interest to the defence community as they could save a lot of time and money collecting and annotating data.
|Title of host publication||Proceedings of the 5th IMA Conference on Mathematics in Defence|
|Number of pages||9|
|Publication status||Published - 23 Nov 2017|
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Multi-modal object detection in non-corresponding imagery using unsupervised techniquesAuthor: Abbott, R., Jul 2021
Supervisor: Martinez del Rincon, J. (Supervisor) & Robertson, N. (Supervisor)
Student thesis: Doctoral Thesis › Doctor of PhilosophyFile