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In the pose detection for a parallel robot based on binocular vision, error detection can be caused by end-effector being obscured by the branch of the mechanism. To solve the problem, a pose detection method for visual blindness based on the direct kinematics compensated by a hybrid optimization RBF neural network (RBFNN) is proposed. Firstly, RBFNN training samples are collected in non-visual blindness, where the direct kinematics is the input sample, and the difference value between direct kinematics and pose detected by binocular vision is the output sample. Then, Grey Wolf Optimization (GWO) algorithm and Levenberg-Marquardt (LM) algorithm optimize the weights in the training process. Finally, the hybrid optimized RBFNN having been trained is applied to compensate the error of direct kinematics to improve the accuracy of pose detection for visual blindness. Experimental results show that compared with the uncompensated pose detection method, when the pose detection method compensated by a hybrid optimization RBFNN is applied, the mean absolute value of error for pose component x, pose component y, pose component z and pose component γ are reduced by 54.4%, 67.7%, 54.7% and 52.9%, respectively; the standard deviation of error for pose component x, pose component y, pose component z and pose component γ are reduced by 52.9%, 62.8%, 51.9% and 58.8%, respectively. The results verify the effectiveness of the proposed method.