###### Abstract
Therefore, to address the challenges of detecting minute defects on the surface of industrial product packaging bags in industrial assembly systems, we propose the small-sample object detection model FSNB_YOLOv8, which is based on an improved YOLOv8 [26]. This model alleviates the issue of long-tailed data distribution and enhances the generalization capability of the model by introducing a fuzzy search interactive data augmentation algorithm [27]. At the same time, the network structure is optimized by replacing the original backbone network with a combination of FasterNet [28] and SPD-Conv [29], reducing the computational complexity and improving the detection accuracy for small target defects. Furthermore, a weighted bidirectional feature pyramid network [30] is introduced to enhance the performance of multiscale feature fusion and improve detection accuracy. Additionally, the NWD [31] loss function is employed to optimize positioning performance and reduce detection errors caused by positional deviations, particularly for small and subtle defects in industrial product packaging systems. The primary contributions of this paper are as follows:  
* To address the challenge of the long-tailed distribution of industrial product packaging bag data in practical industrial scenarios, we introduce an interactive data augmentation algorithm based on fuzzy search. This algorithm effectively increases the number of defective samples by intelligently generating and enhancing them, thereby alleviating the issue of data imbalance and enhancing the generalization capabilities of the model. By leveraging this approach, we can improve the performance of defect detection systems in real-world industrial settings, where data distribution is often imbalanced. * To address the high computational complexity of the C2F module, we have designed a joint network incorporating FasterNet and SPD-Conv, which replaces the original backbone network of YOLOv8. This improvement aims to reduce the computational load and network redundancy while enhancing the accuracy of small target defect detection. By optimizing the network structure, FSNB_YOLOv8 can better adapt to the computational resource constraints of edge devices while maintaining its performance. * To further enhance the performance of multiscale feature fusion, we introduce the weighted bi-directional feature pyramid network (BiFPN). This network effectively enhances the model's ability to detect defects at different scales by integrating deep and shallow information, thereby improving the accuracy and stability of the detection process. * To reduce the sensitivity to defects' positional deviations, this paper employs the Normalized Wasserstein Distance (NWD) loss function. This function can more accurately measure the distance between the predicted bounding boxes and actual boxes, optimizing the model's localization performance and reducing detection errors caused by positional deviations. The remainder of this paper is organized as follows: Section 2 provides an overview of related work on small-sample defect detection and image enhancement. Section 3 details the specific improvement measures proposed in this article. Section 4 presents the experimental results and analysis. Finally, Section 5 discusses and summarizes the results of this study.