Detecting dangerous behaviours and promoting safety in manufacturing using computer vision

The safe operation of forklifts in manufacturing environments is critical for the efficient transportation of goods. However, accidents can occur due to distraction, failure to use Personal Protective Equipment (PPE), and improper handling of the forklift. To improve safety, the authors propose a computer vision solution to monitor forklift operators and their compliance with safety regulations. The model is trained to detect behaviours that could lead to accidents and alert the operator in real time. The proposed solution can be integrated with the forklift's control system, providing immediate feedback to the operator to reduce the risk of accidents. The model uses transfer learning, a technique that leverages pre-trained models to improve the accuracy of the model with limited data. The PoseNet pre-trained model was fine-tuned on a dataset of annotated videos of forklift operators to improve its accuracy in classifying different behaviours. Future work can investigate the integration of the solution with other safety systems to provide a comprehensive safety solution in manufacturing environments.