摘要： 水样袋杂质是指在工业生产中不慎落入袋内的微小异物，包括铁屑、毛发、泥土颗粒等，该类杂质通常因其目标微小、背景复杂且存在严重的文字标识等干扰，导致传统检测方法难以满足工业生产对质量控制的严格要求。为解决上述问题，提出一种面向复杂工业场景下的水样袋杂质检测方法，该方法分别从数据和模型层面进行创新。在数据层面，设计了一套基于双视角交叉验证的自动化采集和检测装置，该装置通过双工业相机和电磁控制系统实现水样袋双面自动检测与智能分拣，并据此装置构建了包含3000张图像的专用数据集WBID-3K，涵盖了真实工业场景下可能出现的各种类型杂质。在模型层面，基于该数据集，提出一个面向跨域特征增强与层级化信息融合的模型WBID-DETR，该模型通过细粒度频域特征优化器强化微小目标的高频特征表达，借助多尺度全域特征融合模块抑制文字标识等干扰，并利用互补特征融合模块补全丢失信息，以此实现对各类微小杂质的精准定位与准确识别。实验结果表明，在自制WBID-3K数据集上，WBID-DETR在准确率和mAP50上比基准模型分别提升了4.2%和3.5%；在包含复杂背景与密集小目标的VisDrone2019公开数据集上，WBID-DETR在准确率和mAP50上比基准模型分别提升了2.5%和3.4%，这充分证明了所提方法对小目标检测任务的泛化性与鲁棒性，为工业质检自动化提供了有效的解决方案。

Abstract: Water sample bag impurities refer to tiny foreign objects accidentally entering the bags during industrial production, such as iron filings, hair, and soil particles. Due to their small size, complex background, and significant interference from text labels, traditional detection methods struggle to meet the strict quality control requirements of industrial production. To address this issue, a water sample bag impurity detection method for complex industrial scenarios is proposed, which innovates at both the data and model levels. At the data level, an automated collection and detection device based on dual-view cross-validation is designed; this device uses dual industrial cameras and an electromagnetic control system to realize automatic double-sided detection and intelligent sorting of water sample bags, and based on this device, a dedicated dataset of 3000 images WBID-3K is constructed, covering all types of impurities that may appear in real industrial scenarios. At the model level, based on this dataset, a model named WBID-DETR for cross-domain feature enhancement and hierarchical information fusion is proposed. This model strengthens the high-frequency feature expression of tiny targets through a fine-grained frequency-domain feature optimizer, suppresses text label interference via a multi-scale global feature fusion module, and complements missing information using a complementary feature fusion module, thereby achieving accurate localization and identification of various tiny impurities. Experimental results show that on the self-built WBID-3K dataset, WBID-DETR achieves 4.2% and 3.5% improvements in accuracy and mAP50 respectively compared to the baseline model; on the public VisDrone2019 dataset containing complex backgrounds and dense small targets, WBID-DETR achieves 2.5% and 3.4% improvements in accuracy and mAP50 respectively compared to the baseline model. This fully demonstrates the generalization and robustness of the proposed method for small target detection tasks, providing an effective solution for automated industrial quality inspection.