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In order to solve the problems of low efficiency and dependability of human experience in shot hole depth recognition by manual interpretation of the massive drilling videos, this paper proposed a repetition counting framework from the perspective of representation object motion, and firstly studied and realized the automatic shot hole depth recognition by adopting the proposed framework by combining multiple deep learning-based models. Firstly, the periodic motion characteristics of the objects in the drilling video were analyzed, and the motion extraction model of the representative object was established based on YOLOv5 and Deep SORT algorithm to obtain the spatiotemporal sequence. Then, the sequence noise and redundant information were filtered according to the spatiotemporal relationship between the drill pipes and workers, to obtain a coherent, smooth, and clean motion sequence. On this basis, a drill pipe motion counting model was established by using the YOLOv5 network to detect the temporal boundary and repetition number of the drill pipe motion. Finally, combined with the pipe motion extraction model and counting model, the comprehensive reliable recognition method of shot hole depth was presented. The shot hole depth recognition trial was carried out on 1029 drilling videos from the Bureau of Geophysical Prospecting INC. The proposed method achieved 90.5% recognition accuracy (Acc) and 0.027 mean absolute error (MAE) with 64 frames per second (FPS) and 0.39 average recognition time ratio (R t). Therefore, the proposed repetition counting framework solved the problem of efficient and intelligent drilling depth recognition of massive drilling videos and thus provided a new idea for repetition counting. • A new repetition counting framework for shot hole depth recognition based on representative object motion was proposed. • The explicit features were added into the optimization of the framework components. • The multi-scale temporal correlation was transformed into process detection to handle the non-stationary period problem. • A sequence denoising method based on spatiotemporal relationship was proposed to handle the noise and redundant information. [ABSTRACT FROM AUTHOR]