Enhancing Synchronization of YOLO-Based Traffic Detection on Low-End Devices by Using the COID Algorithm

The rapid increase in population and vehicle usage intensifies traffic congestion, creating a pressing need for accurate real-time vehicle detection. While the you only look once (YOLO) algorithm enables efficient end-to-end detection, its performance is constrained by hardware limitations, leading to desynchronization on low-end devices. To address this limitation, this study proposes the catch one image detection (COID) algorithm, which restores synchronization without altering the YOLO architecture or requiring retraining. By dynamically adjusting the frame capture interval according to hardware capability, COID ensures real-time alignment between detection and live video streams while reducing deployment complexity. Experimental evaluations on high-, mid-, and low-end devices, including validation on multi-intersection surveillance footage under low-visibility conditions, confirm the robustness and applicability of COID. These findings demonstrate COID’s practicality as a scalable solution for real-world intelligent traffic monitoring.