Deep learning‐based automatic detection of muck types for earth pressure balance shield tunneling in soft ground

Abstract For the earth pressure balance shield, the muck can reflect soil information at the tunnel face to track the change in geologic conditions. Thus, this paper presents a general framework for automatic detection of muck types based on the on‐site surveillance camera using deep learning algorithms. A simplified muck classification method and the corresponding muck recognition criteria are proposed for the muck detection task. The muck detection model (MDM) based on You Only Look Once v4, is established on the muck dataset for Shanghai (MSH) after some optimization treatments. The mean average precision value of 97.73% of MDM is twice that of the original model of 48.47%. The MDM is then applied to Metro Line 14 in Shanghai. Results show that The MDM performs well and meets the real‐time requirements with frames per second of 60, and it outperforms other state‐of‐the‐art detection models both in accuracy and speed.