Robotic grasping in complex environments is challenging due to variations in object positions, orientations, and backgrounds. This study proposes a vision-based method for collaborative robots that integrates object detection, target mask generation, and grasp position estimation to improve grasping reliability. Experiments on a robot platform demonstrate effective target-specific grasping in unstructured multi-object scenes, showing its potential for intelligent manufacturing applications.