Latency Trade-Offs of a Lightweight YOLOv11 Detection Algorithm on Edge Computing Nodes for Factory Safety Decision-Making: An On-Site Experiment in Smart Manufacturing

Abstract

Ensuring real-time safety in smart manufacturing environments increasingly relies on vision-based object detection systems deployed at the edge. However, achieving a balance between inference accuracy and low-latency performance remains a key challenge, particularly when deploying deep learning models on resource-constrained industrial edge devices. To address this, the authors propose Factory-Aware YOLOv11(FA-YOLOv11), a lightweight object detection framework designed for latency-aware safety decision-making in factory settings. The method introduces a pruned and quantized YOLOv11 backbone, augmented with multi-scale feature fusion and a latency-gated decision mechanism to suppress unstable detections. Temporal filtering further enhances precision under noisy conditions. This study highlights the importance of latency–precision co-optimization for edge intelligence in smart factories and provides a deployable solution to improve factory safety through fast and accurate perception.