Fabric defects can significantly impact the quality of a textile product. By analyzing the types and frequencies of defects, manufacturers can identify process inefficiencies, equipment malfunctions, or operator errors. Although deep learning networks are accurate in classification applications, some defects may be subtle and difficult to detect, while others may have complex patterns or occlusions. CNNs may struggle to capture a wide range of defect variations and generalize well to unseen defects. Discriminating between genuine defects and benign variations requires sophisticated feature extraction and modeling techniques. This paper proposes a residual network-based CNN model to enhance the classification of fabric defects. A pretrained residual network, ResNet50, is fine-tuned to classify fabric defects into four categories: holes, objects, oil spots, and thread errors on the fabric surface. The fine-tuned network is further optimized via cuckoo search optimization using classification error as a fitness function. The network is systematically analyzed at different layers, and the investigation of classification results are reported using a confusion matrix and classification accuracy for each class. The experimental results confirm that the proposed model achieved superior performance with 95.36% accuracy and a 95.35% F1 score for multiclass classification. In addition, the proposed model achieved higher accuracy with similar or fewer trainable parameters than traditional deep CNN networks.

中文翻译：

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使用布谷鸟搜索优化的深度残差网络对织物表面缺陷进行分类和系统分析

织物缺陷会严重影响纺织产品的质量。通过分析缺陷的类型和频率，制造商可以识别流程效率低下、设备故障或操作员错误。尽管深度学习网络在分类应用中是准确的，但有些缺陷可能很微妙且难以检测，而另一些缺陷可能具有复杂的模式或遮挡。 CNN 可能很难捕获各种缺陷变化并很好地泛化到看不见的缺陷。区分真正的缺陷和良性变化需要复杂的特征提取和建模技术。本文提出了一种基于残差网络的CNN模型来增强织物疵点的分类。预训练的残差网络 ResNet50 经过微调，可将织物缺陷分为四类：织物表面的孔洞、物体、油斑和螺纹错误。使用分类误差作为适应度函数，通过布谷鸟搜索优化进一步优化微调网络。在不同层对网络进行系统分析，并使用混淆矩阵和每个类别的分类精度来报告分类结果的调查。实验结果证实，所提出的模型在多类分类方面取得了 95.36％ 的准确率和 95.35％ 的 F1 分数的优越性能。此外，与传统深度 CNN 网络相比，所提出的模型通过相似或更少的可训练参数实现了更高的精度。