YOLO11-FGA: Express Package Quality Detection Based on Improved YOLO11

In response to the rapidly increasing volume of express parcels, the challenges of insufficient detection accuracy and slow response speed in express parcel quality detection systems have become prominent. To address these issues, we propose the YOLO11-FGA algorithm, an enhanced version of the YOLO11n model designed to improve both detection accuracy and response speed. The key innovation in this model is the introduction of the FasterNet backbone, which enhances the feature extraction capabilities while maintaining a lightweight design, thus improving overall computational efficiency. Additionally, we incorporate the C3k2_GhostDynamicConv module, which combines the GhostBottleneck and DynamicConv structures to significantly enhance the detection and feature extraction capabilities, particularly for subtle and complex packaging defects. To further improve detection performance, an Auxiliary training head (Aux) is added to the YOLO11 detection head, enhancing multi-scale feature fusion and boosting the accuracy of small target detection, which is essential for identifying minor defects. Experimental results demonstrate that YOLO11-FGA outperforms YOLO11n, with improvements in accuracy (1.1%), recall (1.3%), [email protected] (2.3%), and [email protected]:0.95 (1.4%). These results highlight the superior performance of the YOLO11-FGA algorithm, which offers enhanced detection accuracy, robustness, and computational efficiency, making it a highly effective solution for real-time express parcel quality detection in logistics applications.