With the advancement of smart mines technology, unmanned and Shojinka have received widespread attention, among which unattended crushing station is one of the research directions. To realize unattended crushing station, first of all, it is necessary to detect loose material blockage at the crushing mouth. Based on deep learning (DL) and machine vision (MV) technology, an on-line detection method is studied to trace the blockage in a swift and accurate manner, so that the corresponding detection system can be designed accordingly. The charge coupled device (CCD) industrial camera set above the crushing mouth is used to collect images and input them to the edge computing equipment. The original Single Shot MultiBox Detector (SSD) preprocessing model is trained and optimized before it is combined with the MV technology to detect and then the MV technology is combined to detect whether the crushing mouth is covered. In Ansteel Group GUANBAOSHAN mine, the accuracy of recognition and detection system with human observation was examined for one month, and the tested accuracy is 95%. The experimental results show that the proposed method can detect the crushing mouth blockage in real time, which would solve the problem that the blockage can only be identified by human eyes in traditional method, and provides basic support for the unattended crushing station.