Aiming at the problem of low efficiency and inconsistent standards in visual inspection of aviation lock-wire twisting direction by human in typical maintenance scenarios, an automatic detection model CCF-YOLOv7 is constructed. Using YOLOv7 as the basic model, the convolutional block attention mechanism CBAM is integrated into the SPPCSPC spatial pooling pyramid block; Embedding a CA coordinate attention mechanism at the last three E-ELAN blocks of neck network; Optimizing the bounding box regression loss function CIoU to Focal-EIoU Loss. In addition, we also create an aviation lock-wire twisting direction dataset. Comparison and ablation experiments are conducted on the CCF-YOLOv7 model, and the results show that the CCF-YOLOv7 achieves the highest accuracy of 83.33%. Compared with existing object detection models like YOLOv5s, CCF-YOLOv7 achieves a better detection result in the dataset.