Bridge the gap between practical application scenarios and cartoon character detection: A benchmark dataset and deep learning model

Abstract

The success of deep learning in the field of computer vision makes cartoon character detection (CCD) based on target detection expected to become an effective means of protecting intellectual property rights. However, due to the lack of suitable cartoon character datasets, CCD is still a less explored field, and there are still many problems that need to be solved to meet the needs of practical applications such as merchandise, advertising, and patent review. In this paper, we propose a new challenging CCD benchmark dataset, called CCDaS, which consists of 140,339 images of 524 famous cartoon characters from 227 cartoon works, game works, and merchandise innovations. As far as we know, CCDaS is currently the largest dataset of CCD in practical application scenarios. To further study CCD, we also provide a CCD algorithm that can achieve accurate detection of multi-scale objects and facially similar objects in practical application scenarios, called multi-path YOLO (MP-YOLO). Experimental results show that our MP-YOLO achieves better detection results on the CCDaS dataset. Comparative and ablation studies further validate the effectiveness of our CCD dataset and algorithm.