MODS -- A USV-oriented object detection and obstacle segmentation benchmark

• URL: http://arxiv.org/abs/2105.02359v1
• Date: Wed, 5 May 2021 22:40:27 GMT
• Title: MODS -- A USV-oriented object detection and obstacle segmentation benchmark
• Authors: Borja Bovcon, Jon Muhovi\v{c}, Du\v{s}ko Vranac, Dean Mozeti\v{c}, Janez Per\v{s}, Matej Kristan
• Abstract summary: We introduce a new obstacle detection benchmark MODS, which considers two major perception tasks: maritime object detection and the more general maritime obstacle segmentation. We present a new diverse maritime evaluation dataset containing approximately 81k stereo images synchronized with an on-board IMU, with over 60k objects annotated. We propose a new obstacle segmentation performance evaluation protocol that reflects the detection accuracy in a way meaningful for practical USV navigation.
• Score: 12.356257470551348
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Small-sized unmanned surface vehicles (USV) are coastal water devices with a broad range of applications such as environmental control and surveillance. A crucial capability for autonomous operation is obstacle detection for timely reaction and collision avoidance, which has been recently explored in the context of camera-based visual scene interpretation. Owing to curated datasets, substantial advances in scene interpretation have been made in a related field of unmanned ground vehicles. However, the current maritime datasets do not adequately capture the complexity of real-world USV scenes and the evaluation protocols are not standardised, which makes cross-paper comparison of different methods difficult and hiders the progress. To address these issues, we introduce a new obstacle detection benchmark MODS, which considers two major perception tasks: maritime object detection and the more general maritime obstacle segmentation. We present a new diverse maritime evaluation dataset containing approximately 81k stereo images synchronized with an on-board IMU, with over 60k objects annotated. We propose a new obstacle segmentation performance evaluation protocol that reflects the detection accuracy in a way meaningful for practical USV navigation. Seventeen recent state-of-the-art object detection and obstacle segmentation methods are evaluated using the proposed protocol, creating a benchmark to facilitate development of the field.

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