Real-time Production Line Safety Monitoring Using Deep Learning-based Object Detection and Feature Enhancement

With the deepening of Industry 4.0, the automation and intelligence levels of production lines have significantly improved, placing higher demands on the real-time performance, accuracy, and safety of monitoring. Traditional monitoring systems, relying on manual inspections or simple threshold-based decisions, generally suffer from slow response times, high false alarm rates, and limited intelligence. Therefore, this paper proposes a production-line safety monitoring system that integrates You Only Look Once version 11 (YOLOv11) with a convolutional neural network (CNN). First, the acquired images were preprocessed. Then, YOLOv11 was used to identify workers, equipment, and potential hazards in real time. Next, an enhanced CNN network with multi-scale feature fusion and attention mechanisms was introduced to improve the feature extraction capabilities for small and occluded targets. Finally, the detection results were fused with the CNN-enhanced features to assess safety status. Experiments were conducted using a self-built production-line safety dataset, employing the stochastic gradient descent (SGD) optimizer with momentum 0.9, an initial learning rate of 0.01, weight decay of 0.0005, cosine-annealed learning rate adjustment, a batch size of 32, and training for 200 epochs. mAP@0.5 and detection speed in frames per second (FPS) were used as evaluation metrics for comparison with the evaluated baseline algorithms. The results show that the proposed system achieved an mAP@0.5 of 0.91 and a detection speed of 120 FPS. It also demonstrated robust performance under complex conditions such as shading and varying lighting, supporting its effectiveness and practical application potential in production line safety monitoring.