Vortex plays a crucial role in the formation and maintenance of various flow structures in fluid motion. The identification and detection of vortices are helpful to understand the flow laws. Traditional vortex detection methods have many shortcomings， such as inaccurate definition， heavy dependence on empirical threshold and poor generalization performance， which make vortex detection challenging. In this paper， a vortex detection model based on object detection algorithm is proposed from the perspective of computer vision. Aiming at the problem that the original object detection model has unsatisfactory detection accuracy on slender vortices with extreme aspect ratio， this paper analyzes the data characteristics of two different types of vortices. A feature adaptive module based on deformable convolutional network （DCN） and a slender sample mining method based on improved loss function are proposed. The cylindrical wake vortex and submarine tail vortex data sets are used to verify the proposed model. Experimental results show that the improved model improves the detection accuracy significantly， and the detection accuracy of slender vortex is especially significantly improved， which effectively balances the performance of various types of vortex detection.