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.
Wang et al. (Fri,) studied this question.