基于Efficient-Net的轮胎X光片缺陷检测技术研究

Research on Tire X-ray Film Defect Detection Technology Based on Efficient-Net

为解决传统深度学习网络模型在轮胎X光瑕疵图像检测上识别率低、准确性差的问题,基于特征金字塔网络FPN提出一种多级特征提取网络TWFPN并将其与Ef-ficient-Net目标识别网络融合,得到高度融合语义和细节信息的瑕疵特征向量并改进检测算法流程;通过融合背景信息的检测算法对模型识别结果进行重新判定,得到最终的瑕疵类别和位置信息。对6种轮胎缺陷类型X光图片进行测试表明,改进检测算法显著提高了模型的识别精度,具有良好的应用前景。

In order to solve the problem of low recognition rate and poor accuracy of traditional deep learning network models in tire X-ray defect image detection,this paper proposes a multi-level feature extraction network TWFPN based on the feature pyramid network FPN and integrates it with the Efficient-Net target recognition network to obtain the feature vector of the flaw with a high degree of fusion of semantic and detailed information and improve the detection algorithm process.Through the fusion of the background information,the model recognition result is re-judged,and the final flaw category and location information are obtained.Tests on X-ray images of six types of tire defects show that these improvements have significantly improved the recognition accuracy of the model,and have good application prospects.