The 21st century is the century of oceans. The ocean plays more and more important roles in the world. Oceans are unknown to human beings. Human beings yearn to the resources in the oceans. All of above motivate human beings to explore the oceans. Underwater robotics can be divided into three kinds; Human Occupied Vehicle (HOV), Remotely Operated Vehicle (ROV) and Autonomous Underwater Vehicle (AUV). AUV offers broad operation range and efficient operating type. It has become the trend of underwater robotics. Detection and pose estimation of underwater specific targets is a very important component of the sensing of underwater robotics. It is also of great importance and of high requirements to the sensing of underwater robotics. The detection of underwater specific target refers to that a specific underwater target can be found and its location can be located in the view of the sensor once it appears in the view of the sensor. The location of underwater specific target refers to computing the location and orientation between the underwater robotics and the target. Among sensors used in the detection and pose estimation of underwater targets, cameras are widely adopted due to its relatively low price and high resolution. Detection and pose estimation of underwater specific targets is a key technique to vision-based autonomous underwater docking. AUVs is able to refill energy and being recovered automatically with the help of autonomous underwater docking. In this paper, we aim at autonomous underwater docking and automated underwater recovery. Taking the underwater docking station as the specific underwater target, we study the detection and pose estimation of the underwater docking station. Our research includes: (1) Research on the detection of underwater specific target. In order to solve poor performance of existing methods for detection of underwater specific target, a region-based underwater specific target detection network and a regression-based underwater specific target network were proposed. The contribution of the different data augmentation method for improving detection performance are analyzed in the content of region-based underwater specific target detection network. In the chapter of a regression-based underwater specific target network, the good performance of the proposed region-based underwater specific target network is validated by experiments. We also analyzed its robustness in blurring, color shift, contrast shift and mirror underwater images. Experiments show that the proposed regression-based underwater specific target network achieved better performance than baseline models. (2) Research on domain adaptation in underwater specific target detection. In underwater specific target detection, the training data and the data in practice is not independent identically distributed. There is a domain shift between them. The domain shift results in poor performance of the trained model in practice, which is weak in domain adaptation. In order to solve problems above, a novel Generative Adversarial Network T2FGAN was proposed. The network is able to generate underwater images with given water properties, illumination, pose and configurations. Experiments show that the performance of the model trained on the generated images is improved obviously. (3) Research on extraction of feature points of the underwater specific target. Extraction of feature points of underwater specific target bases on the detection, laying foundation for pose estimation of targets in 3D space. In order to solve the problem of complex ambient light and non-uniform spreading, a Laplacian of Gaussian filter based feature extraction method was proposed. Experiments show that the proposed method is better than the baseline method both qualitatively and quantitatively. (4) Research on pose estimation of underwater specific target in 3D space. Pose estimation of underwater specific target in 3D space computes the location and orientation between the target and the underwater robotics. In some situations, feature points are missing. In order to estimate the location and the orientation in these situations, pose estimation methods in full and partial observation were proposed. The proposed method was validated by the dataset, land test and indoor underwater experiments. Experiments show that the proposed method is accurate and effective, meeting the real requirements. (5) Field experiments validating. The proposed methods were applied to autonomous underwter docking for AUV energy refilling using the static docking station and autonomous underwater docking for AUV recovery using mobile docking station. The proposed methods were validated by field experiments of these two tasks. Experiments show that the proposed methods can meet the requirements in practice.