Abstract: To prevent tobacco leaves from overlapping caused by imperfect bundle-loosening, a method for detecting the loosening degree of tobacco bundles was proposed based on YOLOv5 object detection algorithm. The original images were preprocessed to build a dataset of loosened tobacco bundle images. A Ghost module was added to the backbone network of the original YOLOv5 model to generate redundant feature maps. An ACIN module was added to the bottleneck layer to enhance network feature fusion. Meantime, the loosening degree of tobacco leaves was used to evaluate the loosening degree of tobacco bundle. The YOLOv5 models before and after modification were comparatively tested, and the results showed that on the premise of no significant calculation amount addition, the modified YOLOv5 model reduced the number of network parameter and the size of model by 12.8% and 12.4% respectively, and increased average accuracy by 0.2 percentage points. Compared with object detection models, YOLOv4, Efficientdet-d0 and Faster R-CNN, the modified YOLOv5 model features the highest average accuracy and detection speed and fewer Parameters. This technology could provide a support for promoting the speed and precision of flue-cured tobacco sorting.