TY - JOUR A1 - Ohzeki, Kazuo A1 - Kido, Yoshikazu A1 - Hirakawa, Yutaka A1 - Schneider, Stefan-Alexander T1 - Multi-Element Deep Learning Using False Detection Images for Training Set -Effective For License Plate Detection- T2 - IEICE Technical Report N2 - In deep learning, in order to improve learning performance, preprocessing and ingenuity to combine a plurality of discriminators are performed. It can be inferred that it has elements exceeding the set of learning. Therefore, a configuration to combine multiple recognition elements with low loss will be studied. The advance category classification method is expected to narrow the scope of learning in the next stage. Combining elements specialized for FalsePositive/FalseNegative removal after the positive/negative determination is considered to be effective if the accuracy of the subsequent stage is high. We conducted a license plate recognition experiment by combining these and achieved the best performance for Caltech data. Y1 - 2018 UR - https://opus4.kobv.de/opus4-hs-kempten/frontdoor/index/index/docId/601 UR - https://www.ieice.org/ken/index/ieice-techrep-117-514-e.html SN - 0913-5685 VL - Vol. 117 IS - 514 SP - 25 EP - 30 PB - IEICE ER -