Deep Learning and Detection Technique with Least Image-Capturing for Multiple Pill Dispensing Inspection

In this study, we propose a method to effectively increase the performance of small-object detection using limited training data. We aimed at detecting multiple objects in an image using training data in which each image contains only a single object. Medical pills of various shapes and colors were used as the learning and detection targets. We propose a labeling automation process to easily create label files for learning and a three-dimensional (3D) augmentation technique that applies stereo vision and 3D photo inpainting (3DPI) to avoid overfitting caused by limited data. We also apply confidence-based nonmaximum suppression and voting to improve detection performance. The proposed 3D augmentation, 2D rotation, nonmaximum suppression, and voting algorithms were applied in experiments conducted with 20 and 40 types of pills. The precision, recall, individual accuracy, and combination accuracy of the experiment with 20 types of pills were 0.998, 1.000, 0.998, and 0.991, respectively, and those for the experiment with 40 types of pills were 0.986, 0.999, 0.985, and 0.940, respectively.