Abstract
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.
Abstract (translated)
降解的水下图像降低水下物体检测的准确性。然而，水下图像增强的主要方法主要关注提高视觉方面的指标，这可能不会对水下物体检测任务产生好处，甚至可能导致性能严重下降。为解决这个问题，我们提出了一个双向引导的水下物体检测方法，称为BG-YOLO。在所提出的方法中，网络通过构建一个增强分支和一个检测分支来组织。增强分支包括图像增强子网和一个物体检测子网。而检测分支仅包括一个检测子网。一个特征引导模块连接了两个分支的浅卷积层。在训练增强分支时，增强分支中的物体检测子网指导图像增强子网朝着最有益于检测任务的方向进行优化。训练后的增强分支的浅特征图将输出到特征引导模块，通过一致损失约束检测分支，并通过提示检测分支学习更详细的信息来提高检测性能。因此，检测性能将得到改进。在检测任务期间，只保留检测分支以避免引入额外的计算成本。大量实验证明，在严重降解的水下场景中，所提出的方法显示出明显的检测器性能提升，同时保持出色的检测速度。
URL
https://arxiv.org/abs/2404.08979
