Part grasping detection algorithm based on Mask R-CNN

Aiming at the difficulty of grabbing and recycling scattered stacked parts in the industry, this paper proposes an improved target detection algorithm based on Mask R-CNN. First, by integrating the idea of balanced feature pyramid into the feature extraction network, the loss function is improved and the grasping angle prediction branch is added to the output layer of the network model, Parameterized representation of parts grasping problem combined with depth camera, Finally, the data set is established by this method for network training to realize the optimization of the robot target detection method, which verifies the feasibility of the algorithm.