ABOShips -- An Inshore and Offshore Maritime Vessel Detection Dataset with Precise Annotations

• URL: http://arxiv.org/abs/2102.05869v1
• Date: Thu, 11 Feb 2021 07:05:33 GMT
• Title: ABOShips -- An Inshore and Offshore Maritime Vessel Detection Dataset with Precise Annotations
• Authors: Bogdan Iancu, Valentin Soloviev, Luca Zelioli, Johan Lilius
• Abstract summary: Maritime vessel detection of inshore and offshore datasets is no exception. We collected a dataset of images of maritime vessels taking into account different factors. Vessel instances (including 9 types of vessels), seamarks and miscellaneous floaters were precisely annotated. We evaluated the the out-of-the-box performance of four prevalent object detection algorithms.
• Score: 0.17205106391379021
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Availability of domain-specific datasets is an essential problem in object detection. Maritime vessel detection of inshore and offshore datasets is no exception, there is a limited number of studies addressing this need. For that reason, we collected a dataset of images of maritime vessels taking into account different factors: background variation, atmospheric conditions, illumination, visible proportion, occlusion and scale variation. Vessel instances (including 9 types of vessels), seamarks and miscellaneous floaters were precisely annotated: we employed a first round of labelling and subsequently, we used the CSRT [1] tracker to trace inconsistencies and relabel inadequate label instances. Moreover, we evaluated the the out-of-the-box performance of four prevalent object detection algorithms (Faster R-CNN [2], R-FCN [3], SSD [4] and EfficientDet [5]). The algorithms were previously trained on the Microsoft COCO dataset. We compare their accuracy based on feature extractor and object size. Our experiments show that Faster R-CNN with Inception-Resnet v2 outperforms the other algorithms, except in the large object category where EfficientDet surpasses the latter.

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