Cartoon character face detection is more challenging than face detection because it involves many difficult scenarios. Given the huge differences between different cartoon characters’ faces, this study proposes a cartoon character face detection algorithm, named YOLOv8-DEL. Firstly, the DBBNCSPELAN module is designed based on GELAN fusion BDD to reduce model size and enhance detection performance. Next, a multi-scale attention mechanism called ELA is introduced to improve the SPPF structure and enhance the feature extraction ability of the backbone model. Finally, a new detection head for shared convolution is designed to make the network lighter. At the same time, the original CIoU loss function is replaced by Shape-IoU to improve the convergence efficiency of the model. Experiments are carried out on the iCartoonFace dataset, and ablation experiments are carried out to verify the proposed model. Besides, the proposed model is compared with the YOLOv3-tiny, YOLOv5n, and YOLOv6 models. The mAP of the improved model YOLO-DEL reaches 90.3%, 1.2% higher than that of YOLOv8. The parameters amount is 1.69M, 47% lower than that of YOLOv8. The GFLOPs value is 44% lower than that of YOLOv8. Experimental results show that the proposed method effectively improves cartoon character face detection precision while compressing the network model’s size. Thus, the proposed method has proved to be effective.