Aviation Lock-Wire Twisting Direction Detection Based on CCF-YOLOv7 Model

The chapter explores the critical role of aviation lock-wire in aircraft maintenance, emphasizing the importance of correct twisting direction for fastener security. Traditional visual inspection methods are time-consuming and prone to human error, leading to potential safety risks. The article introduces the CCF-YOLOv7 model, an advanced machine vision solution that integrates the Convolutional Block Attention Module (CBAM) and Coordinate Attention (CA) mechanisms to enhance detection accuracy. The model's performance is evaluated through ablation and comparative experiments, demonstrating significant improvements in precision, recall, and mean average precision (mAP) over existing methods. The chapter also highlights the creation of a multi-scale dataset for aviation lock-wire twisting direction detection, addressing the lack of open-source datasets in this domain. The CCF-YOLOv7 model achieves a detection accuracy of 83.33% and a running speed of 30.1 frames per second, meeting real-time detection requirements. This innovative approach offers a standardized and efficient solution for aviation lock-wire inspection, enhancing maintenance safety and promoting the development of smart civil aviation.