  Underwater degraded images greatly challenge existing algorithms to detect
objects of interest. Recently, researchers attempt to adopt attention
mechanisms or composite connections for improving the feature representation of
detectors. However, this solution does \textit{not} eliminate the impact of
degradation on image content such as color and texture, achieving minimal
improvements. Another feasible solution for underwater object detection is to
develop sophisticated deep architectures in order to enhance image quality or
features. Nevertheless, the visually appealing output of these enhancement
modules do \textit{not} necessarily generate high accuracy for deep detectors.
More recently, some multi-task learning methods jointly learn underwater
detection and image enhancement, accessing promising improvements. Typically,
these methods invoke huge architecture and expensive computations, rendering
inefficient inference. Definitely, underwater object detection and image
enhancement are two interrelated tasks. Leveraging information coming from the
two tasks can benefit each task. Based on these factual opinions, we propose a
bilevel optimization formulation for jointly learning underwater object
detection and image enhancement, and then unroll to a dual perception network
(DPNet) for the two tasks. DPNet with one shared module and two task subnets
learns from the two different tasks, seeking a shared representation. The
shared representation provides more structural details for image enhancement
and rich content information for object detection. Finally, we derive a
cooperative training strategy to optimize parameters for DPNet. Extensive
experiments on real-world and synthetic underwater datasets demonstrate that
our method outputs visually favoring images and higher detection accuracy.

Computer Vision and Pattern Recognition