Deconvolutional Neural Network for Generating Spray Trajectory of Shoe Soles

Abstract

The footwear industry is moving towards automation and intellectualization. To overcome the drawbacks of the high-cost and low-efficiency traditional manual shoe sole gluing process, automatic methods were utilized for generating spray trajectories. Currently, most of the reported automatic methods for generating spray trajectories mainly rely on the outer contour bias method. However, the glue is only applied to the area near the edge/contour of shoe soles and the fixed offset distance in the outer contour bias method cannot adapt to the immense amount of shoe styles with high precision and achieve applicability for irregular and unique sole designs. An intuitive yet logical approach to fulfill the requirements is to utilize the deconvolutional neural network for generating shoe sole spray trajectories. In this work, we treated the glue trajectory prediction as an image-to-image prediction and established a novel deconvolutional neural network to generate shoe sole spray trajectories. The as-proposed deconvolutional neural network for generating spray trajectory offered significant advantages over the existing bias-based methods, including: (1) based on the novel deconvolutional neural network, the proposed method for generating shoe sole spray trajectories exhibits greater applicability to irregular shoe soles, which improves the spray accuracy without compromising the spray efficiency; (2) we discard all the pooling layers, which only consist of convolutional and deconvolutional layers, to preserve more spatial information and achieve higher spray accuracy through end-to-end mapping from shoe sole images to shoe sole spray trajectories, resulting in an improved spray accuracy without sacrificing spray efficiency. The Dice similarity coefficient and Hausdorff distance were used as the evaluation metrics to assess the performance of our approach. Our proposed method showed an ultra-high accuracy and precision with a Dice similarity coefficient over 99.25% and a Hausdorff distance less than 1.2 mm, which are ~10% higher than the spray accuracy of other reported traditional methods. Our findings would bring significant improvements to the field of automatic shoe sole spray trajectory generation, which has the potential to promote the utilization of intelligent technologies in the footwear industry.