融合改进 Padim 建模和 ResNet 网络的 喷涂质量检测算法

为了满足喷涂机器人对于喷涂质量检测的需求,采用迁移学习对改进 Padim 建模和 ResNet 网络进行融合,构建自主喷 涂机器人喷涂质量检测一体化模型。 该模型提取一次图像特征可同时用于缺陷定位和分类。 在缺陷定位端,通过改进 Padim 模型以减少特征冗余所造成网络的计算消耗,首先将 ResNet-18 网络获取的 patch 嵌入向量语义层由原先前 3 层改为单 2 层,然 后特征表达由 100 维降维至 20 维,最后训练正样本得到正态分布模型与测试图像进行缺陷定位。 在缺陷分类端,对预训练 ResNet-18 网络进行负样本二次训练,得到 ResNet-18 分类模型对测试图像进行缺陷分类。 经过实验,将一体化模型移植在 jetson nano 移动端中,参数量仅为 11. 69 M,定位精度 94. 5%,分类准确率高达 99. 6%,在机器人位移速度 0. 02 m/ s 下检测时间 为 0. 730 s,不会出现缺帧漏检情况,满足实时检测的要求。

Spraying quality detection algorithm by fusing improved Padim modeling with ResNet network

In order to meet the needs of spraying robots for spraying quality detection, the improved padim modeling and ResNet network are fused by using transfer learning to build an integrated model of spray quality detection for the autonomous spray robot. The model can be used for defect location and classification at the same time by extracting image features once. At the defect location end, the Padim model is improved to reduce the network computing consumption caused by feature redundancy, and the patch embedding vector semantic layer obtained by the ResNet-18 network is first changed from the original three layers to a single second layer. Then the dimension of feature expression is reduced from 100 to 20 dimensions. Finally, the normal distribution model obtained by training the positive samples and the test image are used for defect location. At the defect classification end, the pre-trained ResNet-18 network is re-trained with negative samples, and the ResNet-18 classification model is obtained to classify the test images for defects. After experiments, the integrated model is transplanted into the Jetson nano mobile terminal. The parameter quantity is 11. 69 M, the positioning accuracy is 94. 5%, and the classification accuracy is as high as 99. 6%. The detection time is 0. 730 s when the robot displacement speed is 0. 02 m/ s, and there will be no missing frame detection, which meets the requirements of real-time detection.