Unsupervised adversarial domain adaptation based on interpolation image for fish detection in aquaculture

•An unsupervised adversarial domain adaptive model is proposed for fish detection.•Proposed method can improve robustness of cross-domain detection in aquaculture.•Interpolation samples fill the distribution gap between the different scenes.•The alignment of local and global features is realized by adversarial training.•Cross-domain experiments are performed to verify the method and prove its validity. Deep-learning-based object detection has brought great convenience to modern intelligent aquaculture. However, its good performance mainly depends on a huge number of labeled training data and the assumption that samples are independent and identically distributed. But in real intensive aquaculture, the acquisition of labeled samples in detection tasks is very time-consuming and laborious. At the same time, due to the influence of light, density, and background complexity, the domain shift caused by different data distribution is inevitable. To solve these problems, we introduce the domain adaptive object detection into aquaculture field for the first time to improve the cross-domain robustness of fish detection. To this end, the interpolation-based unsupervised adversarial domain adaptive fish detection model is proposed in this paper. The proposed model combines the detection network Faster RCNN and three adaptive modules to realize the cross-domain detection of fish. First, the style generation network is used to generate interpolation samples to fill the large distribution gap between the two aquaculture scenes. Then the alignment of local and global features is realized through different levels of adversarial training. Moreover, the focal loss is applied to the global adversarial loss to achieve better global feature alignment. This strategy increases the weight of hard-to-classify samples and enables the feature extraction network to extract more domain-invariant features. We conducted cross-domain adaptation experiments by collecting images from different aquaculture scenes. Compared with the original Faster RCNN and domain adaptation model DA-Faster, the proposed method can not only save the cost of manual annotation, but also effectively improve the detection performance of unlabeled target domain by leveraging the knowledge of the source domain.