小样本驱动特征分段网络的防护材料折痕检测

Protective Material Crease Detection with Small Sample-driven Feature Segmented Neural Network

防刺服能在恐怖袭击、医闹伤害、违法犯罪等事件中有效保护生命安全,然而在生产制造及穿着使用中易产生机械折痕。立足于防护材料折痕缺陷的快速检测需求,创新性地在图像识别方法中提出特征分段网络结构,实现了小样本驱动下防护材料折痕的快速、精准检测功能。通过引入注意力机制和深度可分离卷积模块,并赋予损失函数与优化器两种典型参数,全面提高了特征分段网络模型的检测精度与效率;提出几何信息标注算法,搭建防护材料缺陷可视化检测平台,实现了机械折痕自动精准定位与几何信息输出。模型训练结果表明,特征分段网络模型的准确率可达96.19%,折痕缺陷几何信息标注误差在2%以内,优异的可视化检测功能可拓展到大型工程化自动检测领域。研究工作为下一步构建含有折痕缺陷的防刺装备防护性能预测模型奠定了基础。

Stab-proof clothing can effectively protect life safety in terrorist attacks,medical problems,breaking the law and committing crimes and other incidents,but the mechanical creases of tab-proof clothing are easily produced in production and wearing.Based on the demand for rapid detection of crease defects of protective materials,a small sample-driven feature segmented neural network structure is proposed innovatively in the image recognition method,and the rapid and accurate detection of crease defects is realized.By introducing the attention mechanism and the depth-separable convolution module and giving the loss function and the optimizer two typical parameters,the detection accuracy and efficiency of the feature segmented neural network are improved comprehensively.A geometric information annotation algorithm is proposed and a visual detection platform is built for defect detection of protective materials,realizing the automatic and accurate location of mechanical creases and the output of geometric information.The results show that the accuracy of the model can reach 96.19%,and the annotation error of geometric information is less than 2%.The excellent visual detection function can be extended to the field of large-scale engineering automatic detection.The research work lays a foundation for constructing a protective performance prediction model of the stab-proof equipment with crease defects.