Aluminum Car Door Defect Detection by Using Multi-frame Image Segmentation Techniques

다중 프레임 이미지 분할 기술을 사용한 알루미늄 자동차 도어 결함 감지

AI-based image detection technology offers a promising solution for identifying defects in car doors, significantly improving efficiency compared to traditional human inspections. This paper introduces an advanced automatic defect detection system utilizing camera-recorded datasets and trained models to identify defects in aluminum car doors. Unlike previous models focused on aluminum castings, this is the first application specifically targeting car doors. Despite progress in defect detection research, challenges such as data imbalance, complex defect characteristics, and limited research on aluminum car doors persist. To address these issues, we propose the LKADenseNet201 model, enhancing the DenseNet201 architecture with a large kernel attention mechanism.While doing this, we focus on 3 important issues: image augmentation, channel attention and model evaluation.Our image processing process mainly include image augmentation. With image augmentation, we aimed to make data diversity suitable for the real world by obtaining data from different angles and to eliminate the imbalance between defect and normal images. This improvement boosts the model's ability to perceive contextual features and increases computational efficiency, essential for detailed spatial understanding and time-critical tasks. Our approach not only enhances operator efficiency but also moves towards automating the inspection process.