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The Autonomous Underwater Vehicle (AUV) is a crucial tool for ocean exploration due to its exceptional concealment capabilities and supermobility. Locating and retrieving the AUV to the docking station is an essential aspect of research in autonomous robotics. Building upon the laboratory"s original four-paddle ruddless vector-propelled AUV, we have enhanced the contour by refining Canny edge detection techniques. Additionally, we determine the circle center using the minimum circumferential circle method at optimal thresholds. Through Unity3D simulation and pool testing, it has been demonstrated that this approach is straightforward, practical, and robust. In comparison to traditional AUV designs and conventional image recognition methods, this novel AUV can effectively employ an adaptive threshold segmentation detection method based on monocular vision. Consequently, significant improvements have been achieved in terms of underwater terminal guidance docking accuracy (better than 20 cm) as well as docking success rate (exceeding 80%). These advancements hold substantial application value for AUV energy supply management, data download/upload operations, and equipment maintenance in real-world scenarios.