Journal Article

Comic Image Detection Based on MA‐YOLOv8s

Xin, Hong, Jin, Xun, Li, De, Li, Xuanyou

2026

Overview

In recent years, the plagiarism of comic images has become increasingly prevalent, drawing growing attention to copyright protection within the comic industry. To address the limitations of existing object detection models in capturing the distinctive visual characteristics of comic images, this paper proposes an optimized detection framework, MANGA‐YOLOv8s (MA‐YOLOv8s). Specifically, a large separable kernel attention‐based spatial pyramid pooling (SPPF‐LSKA) module is designed to expand the effective receptive field and enhance multiscale feature aggregation for small‐object detection. The C2f‐DBB module is introduced into the detection head to refine deep feature representation while maintaining lightweight computation. Furthermore, a separated and enhancement attention module (SEAM) is incorporated into the detection heads to improve robustness against scale variation and suppress false detections. Unlike simple combinations of existing modules, these designs form a theoretically motivated and task‐specific integration that adapts the YOLOv8 framework to the structural and stylistic characteristics of comic images. Experiments on the Manga109 dataset demonstrate that MA‐YOLOv8s achieves a 3.7% improvement in mAP and a 3.4% increase in precision compared with YOLOv8s. The proposed method offers both theoretical and practical contributions to the development of efficient detection techniques for comic copyright protection.

• Publisher: John Wiley & Sons, Inc
• Subject: Accuracy / Algorithms / Artificial intelligence / Copyright / Deep learning / Image detection / Modules / Object recognition / Plagiarism / Telematics