Comics in Belgian weekly magazines remain understudied due to their fragmentary presence and the challenges of retrieval within large periodical collections. While comics are crucial artifacts of visual culture, their marginal status in general-interest magazines and their multimodal nature hinder both their discoverability and scholarly analysis. This article addresses the need for systematic identification of comics in mass-digitized archives by developing a semi-automatic detection workflow grounded in computer vision and object detection techniques. Drawing on a representative corpus of six general-interest illustrated magazines published in Belgium between 1934 and 1940, the study applies a distant viewing approach using YOLO models to detect and label comics alongside related visual categories such as cartoons, photographs, and advertisements. The methodology combines expert-driven annotation, collaborative labeling with weighted majority voting, and model evaluation to fine-tune visual classification. Our results highlight the conceptual and technical complexities of defining comics computationally, the importance of contextual metadata, and the benefits of hierarchical and iterative model training. The proposed workflow not only enhances access to hidden visual materials in historical magazines but also contributes to broader discussions on the remediation of cultural heritage collections and the role of computational methods in comics studies.