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Underwater image enhancement is such an important vision task due to itssignificance in marine engineering and aquatic robot. It is usually work as apre-processing step to improve the performance of high level vision tasks suchas underwater object detection. Even though many previous works show theunderwater image enhancement algorithms can boost the detection accuracy of thedetectors, no work specially focus on investigating the relationship betweenthese two tasks. This is mainly because existing underwater datasets lackeither bounding box annotations or high quality reference images, based onwhich detection accuracy or image quality assessment metrics are calculated. Toinvestigate how the underwater image enhancement methods influence thefollowing underwater object detection tasks, in this paper, we provide alarge-scale underwater object detection dataset with both bounding boxannotations and high quality reference images, namely OUC dataset. The OUCdataset provides a platform for researchers to comprehensive study theinfluence of underwater image enhancement algorithms on the underwater objectdetection task.

A Benchmark dataset for both underwater image enhancement and underwater object detection