  Accurate and efficient object detection is crucial for safe and efficient
operation of earth-moving equipment in mining. Traditional 2D image-based
methods face limitations in dynamic and complex mine environments. To overcome
these challenges, 3D object detection using point cloud data has emerged as a
comprehensive approach. However, training models for mining scenarios is
challenging due to sensor height variations, viewpoint changes, and the need
for diverse annotated datasets. This paper presents novel contributions to
address these challenges. We introduce a synthetic dataset SimMining 3D [1]
specifically designed for 3D object detection in mining environments. The
dataset captures objects and sensors positioned at various heights within mine
benches, accurately reflecting authentic mining scenarios. An automatic
annotation pipeline through ROS interface reduces manual labor and accelerates
dataset creation. We propose evaluation metrics accounting for sensor-to-object
height variations and point cloud density, enabling accurate model assessment
in mining scenarios. Real data tests validate our models effectiveness in
object prediction. Our ablation study emphasizes the importance of altitude and
height variation augmentations in improving accuracy and reliability. The
publicly accessible synthetic dataset [1] serves as a benchmark for supervised
learning and advances object detection techniques in mining with complimentary
pointwise annotations for each scene. In conclusion, our work bridges the gap
between synthetic and real data, addressing the domain shift challenge in 3D
object detection for mining. We envision robust object detection systems
enhancing safety and efficiency in mining and related domains.
