What question did this study set out to answer?

This research aims to improve the detection of product damage in unmanned vending systems using a robust framework.

April 27, 2026Open Access

PFL-YOLO: a robust framework for lightweight product damage detection with multi-scale semantic enhancement in unmanned vending systems

Puntos clave

This research aims to improve the detection of product damage in unmanned vending systems using a robust framework.
Developed PFL-YOLO based on YOLOv11n with hybrid features including CSP-PT and C3K2-FM modules.
Employed dynamic convolution based on frequency-domain decomposition to enhance feature representation.
Implemented lightweight optimization techniques to reduce computational complexity.
PFL-YOLO achieved 2.68% improvement in mAP@0.5 compared to baseline.
Achieved 6.8% improvement in mAP@0.5:0.95.
Reduced computational cost by 23.8%, enhancing real-time functionalities.

Resumen

The rapid advancement of unmanned vending systems has created notable challenges in accurately detecting minor product damages, especially in complex situations involving occlusion. Current approaches often struggle to find a suitable balance between achieving high detection accuracy and meeting the necessities of real-time processing. To tackle these issues, this paper introduces PFL-YOLO, an efficient object detection model based on YOLOv11n, specifically designed for recognizing product damage. Within the backbone network, we develop a hybrid feature modeling module known as CSP-PT. By utilizing the inherent cross-stage partial feature reuse mechanism of CSP, this module reduces computational redundancy while incorporating a transformer branch to enhance semantic relationships across spatial dimensions. This structural configuration ensures that the output features maintain compactness while exhibiting enhanced representational strength. Furthermore, we present the C3K2-FM module, which features a dynamic convolution mechanism based on frequency-domain decomposition. This strategy effectively addresses the challenge of spectral homogeneity commonly observed in convolutional kernel weights used in traditional dynamic convolution. Consequently, the semantic connections among multi-scale features are strengthened, significantly improving the synergistic representation of both shallow fine details and deep semantic information. In terms of the detection head, we apply structural-level lightweight optimization and effective regression modeling techniques to diminish computational complexity and inference delay without sacrificing accuracy, thereby enhancing real-time functionalities and simplifying deployment. Experimental findings reveal that PFL-YOLO surpasses the baseline model, achieving enhancements of 2.68% in mAP@0.5 and 6.8% in mAP@0.5:0.95, while also decreasing the computational cost by 23.8%. These results confirm the effectiveness and significant practical value of the proposed approach.

Me gusta

Guardar

Ver artículo completo