Using YOLOv5 for the Detection of Icebergs in SAR Imagery

Abstract

This research aims to analyse the sensitivity of the YOLOv5 object detection algorithm to current issues related to the tracking of icebergs in SAR imagery. To this end a sensitivity study was done on (1) the sensitivity of the algorithm to variations in input image resolution, (2) the sensitivity of the algorithm to variations in contrast between an iceberg and its surroundings and (3) the sensitivity of the algorithm to variations in icebergs size. The results show that the algorithm is very robust against variations in contrast between iceberg and surroundings, but is significantly sensitive to iceberg size. Furthermore, it seems that only by using high resolution images, the spatial features of icebergs can be well distinguished from features of other objects in the ocean. The YOLOv5 algorithm thus shows great potential for iceberg detection applications, but it should be explored if the
current sensitivity to size can be overcome if a more evenly distributed training dataset is used. On top of this, it should be noted that this research only serves as an exploratory analysis on the application of the algorithm and it should thus still be explored if our results based on augmented data, also apply on real data.