Foreign Object Detection Model for Retail Cabinets Under Complex Backgrounds

With the rapid expansion of the unmanned retail ecosystem, real-time foreign object detection (FOD) in smart vending cabinets has become a critical technology for ensuring equipment safety and protecting user rights. However, existing models often face bottlenecks in accuracy when dealing with small targets and occlusion scenarios, and struggle to balance accuracy with speed on edge devices. To address these challenges, this paper proposes an improved model specifically designed for foreign object detection based on the YOLOv11n framework, named YOLOv11n-FOD (foreign object detection). In terms of algorithm design, this paper reconstructs the feature extraction and fusion paradigm. Specifically, the original C3K2 module in the backbone network is replaced with a C3K2-SAC (Spatial Attention Convolution) module incorporating an attention mechanism, which enhances global context modeling capabilities. Subsequently, the CARAFE (Content-Aware ReAssembly of Features) operator is introduced to replace traditional interpolation, significantly improving sensitivity to small targets and textural details. Furthermore, the CBAM (Convolutional Block Attention Module) is integrated into the downsampling stage to suppress background noise while reducing computational redundancy. Notably, these improvements maintain an extremely lightweight architecture, increasing computational overhead by only 0.3 GFLOPs. Experimental results demonstrate that the proposed YOLOv11n-FOD achieves significant performance gains: mAP@50 is increased by 0.4%, and mAP@50-95 is improved by 1.0%. Extensive experiments on the SKU-110K dataset further verify the superior performance of the proposed model. In conclusion, this study effectively balances detection accuracy, model complexity, and inference speed, providing an efficient solution for foreign object detection in smart retail cabinets.