Abstract: Disclosed are s full-form flat-knitted helmet shell preform, the preparation method, and the helmet shell, which belong to the field of helmet material technology. Two different knitting directions of the helmet shell preforms are prepared by means of longitudinal knitting and transverse knitting combined with partial knitting respectively; Meanwhile, reinforced yarn is added during the knitting process of the preforms to obtain the transversely and longitudinally knitted helmet shell preforms with reinforced yarn. The fabric structure of the preforms disclosed in this invention is a flat-knitted three-dimensional fabric with reinforced yarns, which solves the problem of low tensile strength and high elongation of flat-knitted fabrics, and solves the poor impact resistance caused by poor bonding strength between layers of helmet shells to some extent.

Abstract: The disclosure discloses an online detection method of circular weft knitting stripe defects based on a gray gradient method, and belongs to the technical field of textile product detection. The method provides a defect detection and positioning method based on the gray gradient method. Before the detection on a new product, only a model needs to be trained to obtain stripe defect feature images with different stitch types and different stitch densities, and the detection is directly performed in a subsequent process. Defects can be fast and accurately recognized, and a cam position causing the stripe defects can be calculated according to a quantity of shot images between the feature images and a marked image, courses in which marks in the marked image are located, courses in which defects in a defect image are located, and machine operation parameters. Moreover, a defect detection device required by the disclosure can be modified on an original circular knitting machine, so the detection cost is reduced.

Abstract: The disclosure discloses an online detection method of circular weft knitting stripe defects based on a gray gradient method, and belongs to the technical field of textile product detection. The method provides a defect detection and positioning method based on the gray gradient method. Before the detection on a new product, only a model needs to be trained to obtain stripe defect feature images with different stitch types and different stitch densities, and the detection is directly performed in a subsequent process. Defects can be fast and accurately recognized, and a cam position causing the stripe defects can be calculated according to a quantity of shot images between the feature images and a marked image, courses in which marks in the marked image are located, courses in which defects in a defect image are located, and machine operation parameters. Moreover, a defect detection device required by the disclosure can be modified on an original circular knitting machine, so the detection cost is reduced.