Underwater target detection is an important part of ocean exploration, which has important applications in military and civil fields. Since the underwater environment is complex and changeable and the sample images that can be obtained are limited, this paper proposes a method to add the adversarial occlusion network (AON) to the standard Faster R-CNN detection algorithm which called Faster R-CNN-AON network. The AON network has a competitive relationship with the Faster R-CNN detection network, which learns how to block a given target and make it difficult for the detecting network to classify the blocked target correctly. Faster R-CNN detection network and the AON network compete and learn together, and ultimately enable the detection network to obtain better robustness for underwater seafood. The joint training of Faster R-CNN and the adversarial network can effectively prevent the detection network from overfitting the generated fixed features. The experimental results in this paper show that compared with the standard Faster R-CNN network, the increase of mAP on VOC07 data set is 2.6%, and the increase of mAP on the underwater data set is 4.2%.

水下目标探测是海洋勘探的重要组成部分，在军事和民用领域具有重要的应用。由于水下环境复杂多变，可以获取的样本图像有限，因此提出一种将对抗遮挡网络（AON）添加到标准Faster R-CNN检测算法中的方法，该算法称为Faster R-CNN-AON网络。AON网络与Faster R-CNN检测网络具有竞争关系，后者学习如何阻止给定目标，并使检测网络难以正确地对阻止的目标进行分类。更快的R-CNN检测网络和AON网络相互竞争和学习，最终使检测网络对水下海鲜具有更好的鲁棒性。Faster R-CNN和对抗网络的联合训练可以有效地防止检测网络过度拟合生成的固定特征。本文的实验结果表明，与标准的Faster R-CNN网络相比，VOC07数据集上的mAP增加了2.6％，而水下数据集上的mAP增加了4.2％。