Yunwang Li

1. Introduction Recent advancements in deep learning have led to significant progress in object detection across various applications, including autonomous driving, security surveillance, and medical imaging [1]. However, object detection models trained on a source domain often perform poorly when directly applied to a target domain due to domain shift [2]. These shifts arise from differences in object appearance, background, or scale, severely limiting the generalizability of detection models.

1. Introduction Coal is currently the main energy source in China. The intelligent construction of coal mines is of great significance for promoting the transformation and upgrading of the coal industry and achieving high-quality development [1,2]. As of the end of April this year, 1922 intelligent coal mining faces and 2154 intelligent tunneling faces have been built nationwide.

Preprint Article Version 1 This version is not peer-reviewed Version 1 : Received: 26 July 2024 / Approved: 26 July 2024 / Online: 29 July 2024 (10:46:53 CEST) Miao, B.; Ge, S.; Li, Y.; Guo, Y. Method of Motion Planning for Digital Twin Navigation and Cutting of Shearer. Preprints 2024, 2024072204. https://doi.org/10.20944/preprints202407.2204.v1 Miao, B.; Ge, S.; Li, Y.; Guo, Y. Method of Motion Planning for Digital Twin Navigation and Cutting of Shearer. Preprints 2024, 2024072204.