Abstract
Aircraft assembly is challenging to ensure accuracy and feasibility due to its numerous components and stringent quality requirements. The assembly assistant system with computer vision has advanced rapidly. However, there is a lack of the dataset required for object detection because of the confidentiality of aircraft components, resulting in a degraded performance of the assistant system. In this paper, we proposed a mixed aircraft component dataset (MACD), including real photos and synthetic images. We adopted Squeeze and Excitation (SE)-YOLOv5 by introducing the SE-Layer into CSPDrkNet53 to improve object detection accuracy. In addition, we defined the price-performance ratio (PPR) as a measure of dataset quality. We also developed an augmented reality assembly assistant system that offers simple and convenient assembly assistance and can improve assembly efficiency and quality.
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This work was supported by National Natural Science Foundation of China under Grant Number: 52130403.
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Yao, Z., Gao, T., Jiang, X., Zhu, Z. (2023). An Aircraft Assembly System Based on Improved YOLOv5. In: Barolli, L. (eds) Innovative Mobile and Internet Services in Ubiquitous Computing . IMIS 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 177. Springer, Cham. https://doi.org/10.1007/978-3-031-35836-4_3
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DOI: https://doi.org/10.1007/978-3-031-35836-4_3
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