LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark

TL;DR

LaRS is a comprehensive maritime obstacle detection dataset and benchmark with diverse scenes and annotations, enabling improved evaluation and development of obstacle detection methods in maritime environments.

Contribution

The paper introduces LaRS, the first large-scale, diverse maritime panoptic obstacle detection dataset and benchmark, facilitating progress in maritime obstacle detection research.

Findings

27 segmentation methods evaluated

Diverse maritime scenes captured in dataset

Benchmark and online evaluation server provided

Abstract

The progress in maritime obstacle detection is hindered by the lack of a diverse dataset that adequately captures the complexity of general maritime environments. We present the first maritime panoptic obstacle detection benchmark LaRS, featuring scenes from Lakes, Rivers and Seas. Our major contribution is the new dataset, which boasts the largest diversity in recording locations, scene types, obstacle classes, and acquisition conditions among the related datasets. LaRS is composed of over 4000 per-pixel labeled key frames with nine preceding frames to allow utilization of the temporal texture, amounting to over 40k frames. Each key frame is annotated with 8 thing, 3 stuff classes and 19 global scene attributes. We report the results of 27 semantic and panoptic segmentation methods, along with several performance insights and future research directions. To enable objective evaluation, we have implemented an online evaluation server. The LaRS dataset, evaluation toolkit and benchmark are publicly available at: https://lojzezust.github.io/lars-dataset