A Tiny Defect Detection System for Tire Mold Surfaces Based on Consecutive Frames

Abstract

The purpose of the study is to achieve the precise location of each defect on the surfaces of the customized tire mold. Traditional image processing systems typically face significant challenges when processing images, including noise, distortions, and tiny objects, resulting in low computing efficiency and detection accuracy. To address these concerns, a comprehensive defect detection system is developed. The system consists of an inspection platform and a surface defect detection method. In order to achieve highly sensitive detection, an inspection platform and a novel filtering mechanism are proposed. The inspection platform is suitable for various customized tire molds and can be adjusted according to different situations. The effect of noise is effectively reduced by the novel filtering mechanism using a continuous image. Defect features are also prevented from being filtered out. In addition, to remove unnecessary parts of the image, U <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Net is adopted to avoid detecting defective features outside the mold. Finally, a tire mold dataset—STM is set up for training and evaluating U <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Net. The proposed method achieves 83.6% precision and 87.9% recall, which is the best performance among all comparisons. Furthermore, this system can provide defect details for subsequent analysis and automatic defect repair.