A two-stage dynamic multi-object positioning and grasping method is proposed to solve the problem of fast and accurate grasping of various types of dynamic objects on a factory assembly line. In the first stage, the proposed multiscale context-aware single-branch fusion semantic segmentation network is used to obtain the mask area of the target object: first, the feature extraction network adopts a single-branch structure, which reduces the number of network parameters while ensuring the extraction of rich spatial information and high-level semantic information; subsequently, the feature fusion network improves the expression ability of spatial data and semantic information through the bilateral guided feature fusion module; finally, the feature enhancement network is designed, and the feature assisted convergence module is embedded in the shallow and deep networks to accelerate the convergence speed of the network. In the second stage, a quick pose estimation strategy based on contour point detection is applied to predict the optimum posture of the grasping point in the mask region. The test results on the self-built dataset and the pipeline platform grab experiments demonstrate that the proposed method can detect and predict the position and posture of the object grab points in real time and accurately complete the object grab. Furthermore, its segmentation accuracy, prediction time, and grab success rate are better than the comparison method.