Improving Accuracy and Efficiency of Monocular Depth Estimation in Power Grid Environments Using Point Cloud Optimization and Knowledge Distillation

Jian Xiao (), Keren Zhang, Xianyong Xu, Shuai Liu, Sheng Wu, Zhihong Huang and Linfeng Li
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Jian Xiao: State Grid Hunan Electric Power Corporation Ltd., Research Institute, Changsha 410007, China
Keren Zhang: State Grid Hunan Electric Power Corporation Ltd., Research Institute, Changsha 410007, China
Xianyong Xu: State Grid Hunan Electric Power Corporation Ltd., Research Institute, Changsha 410007, China
Shuai Liu: State Grid Hunan Electric Power Corporation Ltd., Research Institute, Changsha 410007, China
Sheng Wu: State Grid Hunan Electric Power Corporation Ltd., Research Institute, Changsha 410007, China
Zhihong Huang: State Grid Hunan Electric Power Corporation Ltd., Research Institute, Changsha 410007, China
Linfeng Li: College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China

Energies, 2024, vol. 17, issue 16, 1-13

Abstract: In the context of distribution networks, constructing 3D point cloud maps is crucial, particularly for UAV navigation and path planning tasks. Methods that utilize reflections from surfaces, such as laser and structured light, to obtain depth point clouds for surface modeling and environmental depth estimation are already quite mature in some professional scenarios. However, acquiring dense and accurate depth information typically requires very high costs. In contrast, monocular image-based depth estimation methods do not require relatively expensive equipment and specialized personnel, making them available for a wider range of applications. To achieve high precision and efficiency in UAV distribution networks, inspired by knowledge distillation, we employ a teacher–student architecture to enable efficient inference. This approach maintains high-quality depth estimation while optimizing the point cloud to obtain more precise results. In this paper, we propose KD-MonoRec, which integrates knowledge distillation into the semi-supervised MonoRec framework for UAV distribution networks. Our method demonstrates excellent performance on the KITTI dataset and performs well in collected distribution network environments.

Keywords: depth estimation; knowledge distillation; semi-supervised (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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