YOLO11s-RDS: Response-Guided Feature Enhancement with Spatial Priors for Lightweight Colorectal Polyp Detection

Accurate and efficient colorectal polyp detection is essential for computer-aided colorectal cancer screening. However, endoscopic
images often suffer from specular reflections, intestinal folds, motion
blur, weak lesion boundaries, and small-scale abnormalities, which introduce severe background interference and increase the risk of missed detections and localization errors. To address these challenges, we propose YOLO11s-RDS, a lightweight colorectal polyp detector built upon YOLO11s. The proposed method redesigns the neck with three complementary modules. First, Response-Guided Calibration Fusion (RGCF) replaces direct feature concatenation with response-aware gated fusion,suppressing shallow pseudo-responses caused by reflections and folds while improving cross-scale semantic consistency. Second, Dual-Pooling Feature Enhancement (DPFE) jointly exploits global average responses and local peak activations to strengthen channel representations associated with small polyps. Third, Spatial Prior-Weighted Convolution (SPWConv) introduces a local center-enhancement prior into downsampling convolutions to mitigate spatial-structure degradation of compact targets. Experiments on a uniformly processed hybrid dataset comprising CVC-ClinicDB, CVC-ColonDB, ETIS-LaribPolypDB, and Kvasir-SEG show that YOLO11s-RDS achieves Precision, Recall, F1-score, mAP50, and mAP50:95 of 0.9018, 0.8729, 0.8871, 0.9113, and 0.6715, respectively. Compared with YOLO11s, it improves Recall, mAP50, and mAP50:95 by 5.51%, 3.77%, and 3.26%, demonstrating stronger robustness in complex endoscopic scenes while maintaining a lightweight design.