The present invention relates to a method for quality control of bobbins.

Systems and methods are disclosed for detecting defects during manufacturing processes for materials and products. The provided systems may utilize imaging units and computer detection algorithms to determine the presence or absence of defects in manufactured materials or products. Detection of defects in material or products by the disclosed systems may prompt intervention in the manufacturing process to correct the source of the defects.

Systems, methods, storage media, and computing platforms for scanning items at the point of manufacturing are disclosed. Exemplary implementations may: receive a first set of images of an item from a first set of camera sources; detect a code in the first set of images; combine, responsive to detecting the code, along a second axis perpendicular to the first axis, the first set of images into a first set of combined images; rotate parallel to the first axis; and combine along the first axis.

The invention relates to a method for characterizing, from a volume image, a fibrous structure having a three-dimensional weaving between a plurality of warp yarns extending along a first direction and a plurality of weft yarns extending along a second direction perpendicular to the first one, the method comprising: a first processing (E10) of the volume image by filtering along a third direction perpendicular to the first and second directions so as to attenuate the periodic patterns along the third direction, obtaining (E20) a two-dimensional image corresponding to an intermediate plane along the third direction of the filtered volume image, a second processing (E31, E41) of the two-dimensional image by filtering along the first or second direction so as to attenuate the periodic patterns, obtaining (E32, E33) a one-dimensional profile representing the positions of warp or weft columns and corresponding to an intermediate line along the first or second direction of the filtered two-dimensional image, and comparing (E33, E43) the one-dimensional profile with a reference profile.

Robots might interact with planar objects (e.g., garments) for process automation, quality control, to perform sewing operations, or the like. It is recognized herein that robots interacting with such planar objects can pose particular problems, for instance problems related to detecting the planar object and estimating the pose of the detected planar object. A system can be configured to detect or segment planar objects, such as garments. The system can include a three-dimensional (3D) sensor positioned to detect a planar object along a transverse direction. The system can further include a first surface that supports the planar object. The first surface can be positioned such that the planar object is disposed between the first surface and the 3D sensor along the transverse direction. In various examples, the 3D sensor is configured to detect the planar object without detecting the first surface.

A smart synchronizing method of a web inspection system for monitoring a moving web. A synchronizing device, at least one slave camera, and at least one lighting device illuminate an area of the web arranged to be imaged by the cameras. The method includes transmitting a synchronizing signal to the slave camera. The synchronizing signal includes at least a start pulse and serial data including additional information. All the cameras are synchronized with each other based on synchronization moment when integration of cameras end. A light synchronizing signal is transmitted to the at least one lighting device indicating a switching on and off times of the at least one lighting device, which switching off time corresponds to the synchronization moment, and calculating a starting time of integration based on an individual integration time of a camera and the synchronization moment common for cameras of the web inspection system.

A method for processing a plurality of radiation protective garments includes receiving a plurality of radiation protective garments from a customer, scanning an identification tag of each received radiation protective garment to identify electronic identification data of each radiation protective garment, and automatically identifying a subset of the radiation protective garments that require performance testing by using the identification data to access a file history of each scanned radiation protective garment, the file history including a performance testing schedule. The method further includes using an x-ray scanning system to scan the subset of the radiation protective garments and to generate x-ray image data, automatically updating the file histories of the subset of the radiation protective garments to include the corresponding x-ray image data, and identifying defects in the corresponding radiation protective garment based on the x-ray image data.

Various attachments for additive textile manufacturing machines are disclosed. In one example, an apparatus is provided which comprises a connector component and a liquid deposition component. The connector component is configured to attach to an additive textile manufacturing machine that produces a textile product. The liquid deposition component is configured to deposit a liquid on one or more materials of the textile product.

A system and method of extracting or inspecting a feature of an object using thermal imaging, and a method of inspecting an object of a garment product. The system includes a source of thermal influence arranged to heat or cool an object; an imager arranged to capture a plurality of images of the object when the object is subjected to the thermal influence; and an image processor arrange to processing the plurality of images and to distinguish a feature of interest from the other portions of the object presented on the plurality of images.

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.