Advanced Obstacle Detection and Distance Estimation for Forklift Operations through Integrated Deep Learning Networks

The safety and efficiency of forklift operations in industrial settings are critically dependent on the accurate detection and precise distance measurement of obstacles. This study introduces an innovative deep learning framework that synergizes advanced computer vision methods for obstacle detection with a novel approach to distance estimation using monocular imaging. By harnessing the capabilities of these techniques, the proposed system significantly enhances the safety protocols during forklift navigation. Our comprehensive experimental evaluation demonstrates notable advancements in the accuracy of obstacle identification and the reliability of distance calculations across a range of obstacle sizes and environmental conditions. The outcomes position this research as a pivotal step towards the automation and optimization of forklift operations.