小样本驱动特征分段网络的防护材料折痕检测

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.