Real‐Time Polyp Detection Using an Enhanced YOLOv12 Framework With Adaptive Feature Routing and Auxiliary Self‐Distillation

Early detection of gastric and colorectal polyps during endoscopic procedures is essential for preventing cancer progression; however, manual identification remains challenging due to subtle lesion appearance, size variability, and the fast pace of clinical workflows. YOLO‐based object detection models have demonstrated strong potential for real‐time computer‐aided detection. Still, limitations persist in multiscale representation, small polyp detection, and maintaining accuracy without increasing computational cost. To address these challenges, we developed an enhanced YOLOv12s‐based imaging framework tailored for endoscopic polyp detection. The proposed system integrates three key innovations: (1) RouteHead, an adaptive multiscale feature routing mechanism that dynamically fuses features from multiple feature‐pyramid levels (P2‐P5), enabling improved detection of small and fine‐grained lesions; (2) auxiliary detection heads employed during training for self‐distillation, enhancing representational learning without affecting inference‐time performance; and (3) task‐specific loss functions, including α‐CIoU for precise localization, edge‐aware loss for boundary refinement, and self‐distillation loss for deeper feature consistency. The model was evaluated on a comprehensive multicenter colonoscopy dataset comprising 2188 images aggregated from four publicly available benchmarks: Kvasir‐SEG, CVC‐ClinicDB, CVC‐ColonDB, and ETIS‐LaribPolypDB. For datasets lacking bounding box annotations, YOLO‐format labels were systematically generated from segmentation masks to ensure consistency in annotations across datasets. Training was performed using a stratified data partitioning strategy with a held‐out independent test set and fivefold cross‐validation to obtain robust performance estimates. The enhanced model achieved a mAP@50–95 of 0.6976, a precision of 0.9300, a recall of 0.9069, and an F1‐score of 0.9182, consistently matching or exceeding the baseline YOLOv12s architecture across validation folds and achieving detection accuracy statistically comparable to that of substantially larger detectors at a fraction of their computational cost. Despite improved accuracy, the proposed framework remains computationally efficient with 8.81 million parameters and 7.92 GFLOPs. By combining adaptive multiscale feature routing with self‐distillation‐based training, the proposed imaging system effectively overcomes key limitations of existing real‐time endoscopic detection approaches. The strong balance between detection accuracy, robustness across multiple datasets, and computational efficiency makes the framework suitable for practical computer‐aided endoscopy systems, with the potential to enhance early detection rates and reduce cognitive burden on clinicians during routine examinations.