Though the open-air tracker has achieved an advanced level, its design is still a challenging task for degraded underwater images. Using underwater enhancement technology can improve the performance of underwater trackers. However, most underwater image enhancement methods focus on improving the visual effect rather than serving the tracker better. Therefore, we intended to explore a simple but powerful image domain-adaptive method to improve Stark’s performance by enhancing Stark’s input images. Specifically, it consists of underwater image adaptation network (UIAN) with double heads and adaptation block based on scene estimation (ABSE) that consists of three independent image processing modules without deep learning. UIAN is used to predict the category of image domain and parameters of ABSE. ABSE decodes the parameters and sequentially process underwater images in each module. The training of UIAN is independent of the training of the tracker. After training the class prediction head of UIAN first, freezing its weights, by initializing and tracking in one enhanced image and computing tracker’s loss, the parameter head can be trained to make sure UIANs hyperparameters can match the Stark tracker. The UStark proposed can adaptively process clear and degraded underwater images. Compared with Stark, UStark has improved the accuracy and success rate in typical underwater environments by 3.7% and 1.5% (blue), 5% and 3.4% (yellow), and 5.4% and 3.3% (dark), respectively. In addition, compared with other underwater image enhancement methods, our method can enhance the performance of the tracker in more categories of underwater images.
The autonomous perception of unmanned surface vessels in the natural sea environment has improved significantly; however, it is adversely affected by haze. Improving the U-Net network can retain and restore spatial information effectively and improve image dehazing. However, many dehazing algorithms that use the U-Net network as the main architecture face problems, such as distorted images and blurred contours of small distant objects. To overcome these limitations, we propose a generative adversarial network (GAN)-U-Net++ network that uses the U-Net++ network with a coordinate attention mechanism as the main part of a GAN generator. We used the coordinate attention mechanism to perform horizontal and vertical pixel level pooling to improve the attention on small targets in hazy images. The addition of the edge preservation loss function and image gradient loss function to small targets improved the sharpness of their edges. A Markov discriminator was used to evaluate the authenticity of the images. Several experiments were conducted to compare the proposed method with existing dehazing algorithms. The structural similarity value and peak signal-to-noise ratio increased by 15.74% and 29.14%, respectively.
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