COMPARING CNN-BASED OBJECT DETECTORS ON TWO NOVEL MARITIME DATASETS

Vessel detection studies conducted on inshore and offshore
maritime images are scarce, due to a limited availability of
domain-specific datasets. We addressed this need collecting
two datasets in the Finnish Archipelago. They consist of images
of maritime vessels engaged in various operating scenarios,
climatic conditions and lighting environments. Vessel
instances were precisely annotated in both datasets. We evaluated
the out-of-the-box performance of three state-of-the-art
CNN-based object detection algorithms (Faster R-CNN [1],
R-FCN [2] and SSD [3]) on these datasets and compared them
in terms of accuracy and run-time. The algorithms were previously
trained on the COCO dataset [4]. We explore their
performance based on different feature extractors. Furthermore,
we investigate the effect of the object size on the algorithm
performance. For this purpose, we group all objects in
each image into three categories (small, medium and large)
according to the number of occupied pixels in the annotated
bounding box. Experiments show that Faster R-CNN with
ResNet101 as feature extractor outperforms the other algorithms.