MODS—A USV-Oriented Object Detection and Obstacle Segmentation Benchmark
| Autor: | Janez Perš, Jon Muhovic, Matej Kristan, Dean Mozetic, Dusko Vranac, Borja Bovcon |
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| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Mechanical Engineering Computer Science - Computer Vision and Pattern Recognition Context (language use) Object detection Field (computer science) Machine Learning (cs.LG) Computer Science Applications Inertial measurement unit Obstacle Automotive Engineering Benchmark (computing) Computer vision Segmentation Artificial intelligence business Protocol (object-oriented programming) |
| Zdroj: | IEEE Transactions on Intelligent Transportation Systems. 23:13403-13418 |
| ISSN: | 1558-0016 1524-9050 |
| Popis: | 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 hinders 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. Nineteen 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. The proposed dataset, as well as evaluation routines, are made publicly available at vicos.si/resources. 16 pages, 15 figures. The dataset, as well as the proposed evaluation protocols, are published on our website: https://www.vicos.si/resources/ |
| Databáze: | OpenAIRE |
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