Underwater Image Enhancement and Object Detection: Are Poor Object Detection Results On Enhanced Images Due to Missing Human Labels?

Underwater image enhancement has led to mixed performance when used in conjunction with object detectors. Some works report an improvement in object detection performance when used in conjunction with image enhancement, while other works show that enhancement degrades detection performance. In this work, we identify and attempt to quantify a confounding factor to reconcile these contradictory results: we show that poor image quality leads to low quality labels, which confounds performance assessments done with respect to these noisy labels. Application of image enhancement during the human labeling procedure recovers previously missed labels for less-biased performance assessment. We find that on the test set of the Rethinking general Underwater Object Detection (RUOD) dataset, re-annotation of enhanced images shows a mean increase of 9 labels per image, which leads to a 5 percentage point increase in precision during object detection with a YOLO-NAS model.