  Degraded underwater images decrease the accuracy of underwater object
detection. However, existing methods for underwater image enhancement mainly
focus on improving the indicators in visual aspects, which may not benefit the
tasks of underwater image detection, and may lead to serious degradation in
performance. To alleviate this problem, we proposed a bidirectional-guided
method for underwater object detection, referred to as BG-YOLO. In the proposed
method, network is organized by constructing an enhancement branch and a
detection branch in a parallel way. The enhancement branch consists of a
cascade of an image enhancement subnet and an object detection subnet. And the
detection branch only consists of a detection subnet. A feature guided module
connects the shallow convolution layer of the two branches. When training the
enhancement branch, the object detection subnet in the enhancement branch
guides the image enhancement subnet to be optimized towards the direction that
is most conducive to the detection task. The shallow feature map of the trained
enhancement branch will be output to the feature guided module, constraining
the optimization of detection branch through consistency loss and prompting
detection branch to learn more detailed information of the objects. And hence
the detection performance will be refined. During the detection tasks, only
detection branch will be reserved so that no additional cost of computation
will be introduced. Extensive experiments demonstrate that the proposed method
shows significant improvement in performance of the detector in severely
degraded underwater scenes while maintaining a remarkable detection speed.
