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.

中文翻译：

---

基于深度学习的软土土压平衡盾构掘进渣土类型自动检测

对于土压平衡盾构，渣土可以反映掌子面土体信息，跟踪地质条件的变化。因此，本文提出了一种基于现场监控摄像头使用深度学习算法自动检测渣土类型的通用框架。针对渣土检测任务提出了一种简化的渣土分类方法和相应的渣土识别准则。基于 You Only Look Once v4 的渣土检测模型 (MDM) 在上海 (MSH) 渣土数据集上经过一些优化处理后建立。MDM的平均精度值为97.73%，是原始模型48.47%的两倍。MDM随后应用于上海的地铁14号线。结果表明，MDM性能良好，达到每秒60帧的实时性要求，