When evaluating the performance of the object detection, two indices, mean average precision (mAP) and mean intersection over union (mIoU), were mainly used. mAP is used to measure the accuracy of object detection model, and the closer the value is to 1.0, the more accurate the model is. A mAP of more than 0.7 seemed to be regarded as a good value in other studies, but there is no clear criterion. A mIoU of more than 0.7 is regarded as a good value, and the mAP the mIoU obtained in this study are 0.71 and 0.72, respectively. Considering these, the performance of this learning system can be considered to be high. The values of the hyperparameters were determined from the results of preliminary experiments with various combinations of values. Learning with this combination yielded superior mIoU and mAP values than other combinations.

In the results of this study, the AP of each implant system varies from 0.51 for Genesio to 0.85 for MK III/IIIG, and the mAP is 0.71. The TP ratios also vary from 0.50 for Genesio to 0.82 for MK III/IIIG. These differences are caused by the number of implants, their locations, and their similarity of shape. When selecting implant systems to recognize in this study, frequently used implant systems were selected because the number of implants seemed to be one of the most important factors. In fact, both AP and TP ratio of Genesio, which was the least number of images, were the minimum value, and those of MK III/IIIG were the maximum. About 1300 panoramic images and a total of 3000 implant images were used, but these numbers were not enough to recognize all the implant systems included. To increase the learning performance, a sufficient number of implant images are necessary.