<scp>SC</scp>‐<scp>YOLO</scp>: Robust Multi‐Scale Small Object Detection for Intelligent Transportation

ABSTRACT Existing multi‐scale vehicle detection methods often falter in crowded traffic scenarios—particularly when it comes to locating small vehicles, resolving occlusions, and adapting to scale variations—which leads to a marked drop in overall accuracy. To overcome these challenges, we introduce SC‐YOLO , a lightweight detection framework built upon YOLOv10n and optimized for greater efficiency. First, we replace the standard downsampling between the backbone and neck with a Space‐to‐Depth Convolution (SPDConv) module, preserving fine‐grained details in the lower levels of the feature pyramid so that cues for small targets remain intact. Next, we propose a Context‐Guided Rectangular Feature Pyramid Network (CGRFPN) equipped with a self‐calibration mechanism; by enabling cross‐scale interactions and adaptive feature‐map calibration, it significantly enhances multi‐scale fusion. Finally, guided by extensive empirical evaluation, we adopt the Wise‐IoUv3 dynamic loss function, whose adaptive gradient allocation refines bounding‐box regression. On the Pascal VOC, KITTI, and Cars datasets, SC‐YOLO attains mAP@50 scores of 79.0%, 87.3%, and 74.6%, respectively—improving upon the YOLOv10n baseline by 2.5%, 2.1%, and 2.3%. Crucially, it maintains high accuracy under challenging traffic conditions, especially for small‐vehicle detection and occlusion resolution, while scaling more efficiently, requiring fewer computations than other models with comparable parameter counts. These combined advantages underscore SC‐YOLO's resource‐efficient design and its practicality for intelligent transportation and autonomous‐driving applications.