Identify Subtle Fall Hazards Using Transfer Learning

Computer vision is increasingly used for fall safety monitoring, but it struggles in subtle hazard scenarios, causing delays in hazard detection. Therefore, this issue was addressed in this study using transfer learning on pre-trained models that were fine-tuned with target datasets to enhance accuracy. We tested two scenarios—“scaffolding transverse brace installation” and “correct safety lifeline hook-up”—with MobileNet v2, GoogleNet, Inception v3, and ResNet-50. GoogleNet achieved an accuracy of 95.2% in brace installation recognition, while MobileNet v2 and Inception v3 achieved an accuracy of 96% for lifeline hook-up recognition, demonstrating excellent capability in complex hazard detection.