摘要
In modern industrial production,foreign object detection in complex environments is crucial to ensure product quality and production safety.Detection systems based on deep-learning image processing algorithms often face challenges with handling high-resolution images and achieving accurate detection against complex backgrounds.To address these issues,this study employs the PatchCore unsupervised anomaly detection algorithm combined with data augmentation techniques to enhance the system’s generalization capability across varying lighting conditions,viewing angles,and object scales.The proposed method is evaluated in a complex industrial detection scenario involving the bogie of an electric multiple unit(EMU).A dataset consisting of complex backgrounds,diverse lighting conditions,and multiple viewing angles is constructed to validate the performance of the detection system in real industrial environments.Experimental results show that the proposed model achieves an average area under the receiver operating characteristic curve(AUROC)of 0.92 and an average F1 score of 0.85.Combined with data augmentation,the proposed model exhibits improvements in AUROC by 0.06 and F1 score by 0.03,demonstrating enhanced accuracy and robustness for foreign object detection in complex industrial settings.In addition,the effects of key factors on detection performance are systematically analyzed,providing practical guidance for parameter selection in real industrial applications.
在现代工业生产过程中,复杂环境下的异物检测对于保障产品质量和生产安全具有重要意义。基于深度学习的图像处理检测系统在实际应用中往往面临高分辨率图像处理难度大以及复杂背景下检测精度不足等挑战。针对上述问题,本研究采用PatchCore无监督异常检测算法,并结合数据增强技术,以提升检测系统在不同光照条件、不同视角及不同目标尺度下的泛化能力。以电力动车组(electric multiple unit,EMU)转向架为典型复杂工业检测场景,对所提出的方法进行验证,构建了包含复杂背景、多种光照条件和多视角信息的数据集,用于评估检测系统在真实工业环境中的性能。实验结果表明,所提出的方法在该数据集上的受试者工作特征曲线下面积(the area under the receiver operating characteristic curve,AUROC)平均达到0.92,F1 score达到0.85。与原始模型相比,结合数据增强后的模型在AUROC和F1 score上分别提升了0.06和0.03,显著提高了复杂工业环境下异物检测的准确性与稳健性。此外,本研究系统分析了关键因素对检测性能的影响,为实际工业应用中的模型参数选择提供了参考依据。
