Reliable detection of safety helmets is essential for ensuring personnel protection in large-scale outdoor operations. However, recognition becomes difficult when monitoring relies on low-resolution or compressed video streams captured by fixed or mobile platforms such as UAVs—conditions commonly encountered in intelligent transportation and urban surveillance. This study proposes a super-resolution-enhanced detection framework that integrates video super-resolution with ROI-guided inference to improve the visibility of small targets while reducing computational cost. Focusing on a single, carefully selected VSR module (BasicVSR++), the framework achieves an F1-score of 0.904 in helmet detection across multiple low-quality surveillance scenarios. This demonstrates the framework’s effectiveness for robust helmet monitoring in low-resolution and compressed surveillance scenarios.
He et al. (Sat,) studied this question.