Tsinghua University

Fuchun Sun

Huaping Liu

Bin Fang

The present invention proposes a fabric defect detection method based on multi-modal deep learning. First, a tactile sensor is placed onto the fabric surface with different defects to collect the fabric texture images, a camera is used to collect the corresponding fabric external images, and a fabric external image and a fabric texture image constitute a set of fabric detection data; then, a feature extraction network and a multi-modal fusion network are connected to establish a classification model based on multi-modal deep learning, which uses the fabric texture image and fabric external image in each set of collected fabric detection data as input, and the fabric defect as output; said classification model is trained using the collected fabric detection data; finally, the trained classification model is used to detect the fabric defect. The present invention employs vision-touch complementary information, which can greatly improve the accuracy and robustness of detection.

Fabric defect detection method based on multi-modal deep learning

The invention proposes an automatic throat swab sampling system, which comprises an automatic to-be-tested person identity information checking and collection prompting unit, a to-be-tested person head positioning unit, a navigation positioning unit which enters the oral cavity of a to-be-detected person along with the tail end of the sample collection execution unit for collecting a throat image and determining depth information between a depth sensor and the throat of the to-be-detected person, a sample collection execution unit including a three-degree-of-freedom guide rail type device and a one-degree-of-freedom end execution mechanism, an automatic throat swab loading and unloading unit and a remote monitoring unit which are arranged on a working platform. In a throat swab collecting process, the real-time information of all units communicates with the remote monitoring unit, and medical staff monitors the collected person and all the units in real time through a computer in a remote safety room, so that measures can be taken timely to guarantee the safety and effectiveness of the whole process when an accident occurs. The system realizes the full-automatic throat swab collection process.

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Beijing, China

Fuchun Sun