Improved textile-vamp punching points location using CNN and priori regional template matching in the footwear industry

The accurate and efficient positioning of punching points on textile-vamps is critical for successful footwear manufacturing. Manual methods often lead to errors, increased labor costs, and reduced efficiency. To address these challenges, this paper takes a full account of punching point layout regularity in the vamp and proposes an approach for locating punching points using priori regional template matching and convolutional neural network. Least-squares ellipse fitting is used for coordinate system establishment based on a fixed pattern of 3D additive on the vamp. This facilitates the description of the placement and orientation of the vamp. Then, the prior region and template images are extracted and recorded in accordance with the relative positional relationship between punching points and the vamp. This can limit the template search to designated regions and improve the speed and accuracy of punching point location. Leveraging the determined coordinates system, the extracted prior regions universally applicable to vamps in any placement and orientation. To automatize the adjustment of punching point matching strategies, a textile-vamp classification CNN model is trained to distinguish between different vamp types. Experiments demonstrate the effectiveness of the proposed method on various vamp types and placement, achieving a high accuracy rate of 98%. Therefore, this research offers a promising solution for practical applications in the footwear industry, providing accurate and rapid textile-vamp punching point location.