Aero-engine defect detection by integrating attention and multi-scale features

Le résumé fourni par la source

The structural integrity of aircraft engines is related to flight safety. At present, aircraft engine defect detection based on borescope technology is mainly manual operation. In order to improve the detection accuracy and efficiency, an intelligent aircraft engine defect detection algorithm that integrates attention and multi-scale features is proposed to assist borescope work. First, to address the class imbalance problem of defect samples in the original borescope image, a multi- sample fusion data enhancement method based on geometric transformation and Poisson image editing is used to enrich small sample images and construct a defect dataset. Then, the coordinated attention module (CA) is integrated into the baseline network YOLOv5 to emphasize the extraction of defect features and enhance the network's distinction between defect targets and complex backgrounds. A weighted bidirectional feature pyramid structure (BiFPN) is constructed in the neck network to complete higher-level feature fusion and improve the expression ability of multi-scale targets. Finally, the bounding box regression loss function is defined as the EIOU loss to achieve fast and accurate positioning and identification of defect targets. Experimental results show that the average accuracy of defect detection of the proposed algorithm reaches 89.7%, which is improved compared with the baseline network 6.3%, and the size of the trained model is only 14.0 M. Therefore, the proposed method can effectively detect the main defects of aircraft engines.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.