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A multi-pill box attitude estimation method based on the YOLOv3 deep learning algorithm and the EPnP algorithm is proposed to deal with requirements for location and attitude estimation of pill boxes in the manipulator-gripping operation for pill boxes. This method is constructed by the multi-pill box location part and the attitude estimated part. First, the YOLOv3 algorithm is used to achieve the fast and accurate location of pill boxes, and located pill boxes are distinguished by location boxes. Then, feature extraction and feature matching are performed and the homography matrix is estimated. Pixel coordinates of four corner points in the pill-box plane are obtained by transforming the perspective matrix of the homography matrix, and these pixel coordinates are used as 2D points for EPnP solution. 3D coordinates of pill boxes are constructed under the camera frame by using pill boxes’ prior size information to compute postures of pill boxes. A comparative experiment for box attitude accuracy is designed based on the OptiTrack system, and experimental results show that the algorithm fully utilizes the advantages of the YOLOv3 algorithm e.g., high speed and accuracy, and has good attitude estimation accuracy. The overall computational speed reaches 15FPS, the estimated average error is less than 0.5 degree.