A Vision-Based Bird's Eye View Representation Network for 3D Objects in Open-Pit Mining Area

Autonomous transportation systems, which have reconstructed open-pit mining operations, depend on accurate and real-time obstacle detection in challenging environments. Existing methods often exhibit limitations in accuracy due to their reliance on traditional image-based techniques, leading to errors and incomplete results. To address these issues, we developed a vision-based 3D object detection algorithm designed for open-pit mining environments. Our approach leverages the BEVDepth model, enabling accurate 3D object recognition using monocular camera input. Moreover, camera parameters are integrated to enhance resilience and flexibility across diverse mining environments. Last, our algorithm was implemented on a newly customized open-pit mining dataset. The experimental results verify the algorithm efficiency by attaining a high mean Average Precision score (m $A$P) of 67% while offering real-time performance with inference times as short as 25ms per frame. It enables accurate and efficient obstacle detection in autonomous mining vehicles and contributes to the development of safer and more productive mining operations.