## 2 Related Work  
Research on defect detection based on deep learning often relies on large-scale datasets to train network models, enabling the precise extraction of defect features [32]. However, in industrial pipeline practices, obtaining defect data often faces numerous challenges, such as high human and time costs, which render traditional large-scale data collection methods impractical. To address this issue, researchers have actively explored various strategies, including metric learning, data augmentation, transfer learning, and model fine-tuning, to efficiently perform surface defect detection using deep learning with limited sample data [33]. By leveraging these techniques, it is feasible to achieve accurate defect detection in industrial settings, despite the scarcity of labeled data. This approach holds promise for enhancing quality control and improving production efficiency in various manufacturing industries.  
[PERSON] et al. [34] designed a deep Siamese semantic segmentation network that combines the similarity measurement of the Siamese network with the encoder-decoder semantic segmentation network for PCB welding defect detection. This approach further alleviates the overfitting problem caused by insufficient defect samples. [PERSON] [35] proposed a weakly supervised machine vision detection method based on artificial defect simulation that applied artificial datasets to deep learning recognition algorithms and fine-tuned the initial model for retraining. [PERSON] [36] introduced a knowledge reuse strategy to train a convolutional neural network (CNN) model. This strategy incorporates model-based transfer learning and data augmentation to achieve high accuracy with limited training samples. [PERSON] [37] utilized a single-stage detection model, YOLO, to detect steel surface defects. By integrating shallow features, they improved the detection performance of YOLO for small-sample defects. [PERSON] [38] proposed a fabric defect detection framework based on Generative Adversarial Networks (GANs). This framework trains multilevel GANs to synthesize realistic defects in new defect-free samples and incorporates the generated defects into specific locations to better detect defects under different conditions. [PERSON] [39] developed a semi-supervised Generative Adversarial Network (SSGAN) that can achieve more accurate segmentation results at the pixel level. It leverages unlabeled images to enhance segmentation performance and reduce the burden of data-labeling tasks. He [40] introduced a novel method based on weakly supervised deep learning that designs an encoder for feature extraction and two decoders for two related tasks. This approach accurately segments and locates defects on textured surfaces.  
In this paper, we address the challenges of detecting subtle defects on the surface of industrial product packaging bags in industrial assembly systems. To this end, we introduce a fuzzy search interactive data augmentation algorithm that effectively alleviates the problem of long-tailed data distribution and enhances the generalization capabilities of our model. Furthermore, we optimize the network structure by replacing the original backbone network with a combined FasterNet and SPD-Conv architecture. This optimization not only reduces the computational complexity but also improves the accuracy of detecting small target defects. Additionally, we introduce a weighted bidirectional feature pyramid network (BiFPN) to enhance the multiscale feature fusion performance, further boosting detection accuracy. Lastly, we adopt the Normalized Wasserstein Distance (NWD)loss function to optimize localization performance, minimizing detection errors caused by positional deviations. By implementing these comprehensive strategies, our model effectively addresses the challenges of defect detection in industrial assembly systems, providing a robust and efficient solution for identifying subtle defects on the surface of industrial product packaging bags.