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CCDaS: A Benchmark Dataset for Cartoon Character Detection in Application Scenarios

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Digital Multimedia Communications (IFTC 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2067))

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Abstract

Deep learning’s achievements in computer vision have poised cartoon character detection (CCD) as a promising tool for intellectual property protection. However, due to the lack of suitable cartoon character datasets, CCD is still a less explored field and there are many issues need to be addressed to meet the demands of practical applications such as merchandise, advertising, and patent examination. In this paper, we introduce CCDaS, a comprehensive benchmark dataset comprising 55,608 images of 524 renowned cartoon characters from 227 works, including cartoons, games, and merchandise. To our knowledge, CCDaS is the most extensive CCD dataset tailored for real-world applications. Alongside, we also provide a CCD algorithm that can achieve accurate detection of animated images in complex 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.

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Authors and Affiliations

  1. School of Information and Communication Engineering, Communication University of China, Beijing, China

    Zelu Qi, Da Pan, Tianyi Niu, Zefeng Ying & Ping Shi

Authors
  1. Zelu Qi
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  2. Da Pan
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  3. Tianyi Niu
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  4. Zefeng Ying
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  5. Ping Shi
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Corresponding author

Correspondence to Ping Shi .

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Editors and Affiliations

  1. Shanghai Jiao Tong Univeristy, Shanghai, China

    Guangtao Zhai

  2. Shanghai Jiao Tong University, Shanghai, China

    Jun Zhou

  3. Communication University of China, Beijing, China

    Long Ye

  4. Shanghai Jiao Tong University, Shanghai, China

    Hua Yang

  5. Shanghai University, Shanghai, China

    Ping An

  6. Shanghai Jiao Tong University, Shanghai, China

    Xiaokang Yang

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Qi, Z., Pan, D., Niu, T., Ying, Z., Shi, P. (2024). CCDaS: A Benchmark Dataset for Cartoon Character Detection in Application Scenarios. In: Zhai, G., Zhou, J., Ye, L., Yang, H., An, P., Yang, X. (eds) Digital Multimedia Communications. IFTC 2023. Communications in Computer and Information Science, vol 2067. Springer, Singapore. https://doi.org/10.1007/978-981-97-3626-3_27

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