Quality Control in Umbrella Manufacturing Based on Object Detection: Current Applications and Challenges

Abstract

With the increasing complexity of umbrella manufacturing processes, traditional quality inspection methods are facing significant challenges in terms of accuracy and efficiency. This paper systematically reviews the current applications and future trends of object detection technologies in addressing quality control challenges in umbrella manufacturing, such as the heterogeneous integration of multiple components and dynamic deformation of flexible materials. By analyzing typical defect characteristics like misaligned umbrella printing and rib assembly errors and leveraging the principles of industrial vision inspection technology alongside the advantages of deep learning algorithms, the study highlights technological breakthroughs in complex texture recognition, occlusion localization, and real-time processing achieved through object detection. The paper provides a technical reference for the digital transformation of the umbrella industry and offers valuable guidance for advancing intelligent quality inspection technologies from theoretical breakthroughs to industrial implementation.