基于多模型级联的轻量级缺陷检测算法

基于深度学习技术的缺陷检测算法往往因为网络参数较多而需要大量的图像样本去训练模型，但是在工业生产过程中缺陷产品数量极少，采集大量缺陷数据图像费时又费力。针对这一难题，本文提出了一种基于多模型级联的轻量级缺陷检测算法，它采用监督学习的训练方式，通过少量缺陷样本就可以获得较好的检测效果。首先，使用CBAM注意力残差模块代替常规卷积层进行特征提取，以聚焦缺陷特征，强化网络对缺陷的表征能力；其次，设计了SE-FPN模块，促进各级特征之间有效融合，提高网络对缺陷的分割效果，尤其是对小缺陷的分割效果；最后，在训练阶段，采用监督学习方式对本文所提的多模型算法网络进行训练。实验结果表明，本文所提算法在KolektorSDD数据集上的检测准确率高达99.28%，每张图像的平均检测时间仅需10.5ms，不但充分满足了工业检测行业高精度、实时性的要求，同时，还能实现对缺陷区域精准定位。因此，本文的研究内容非常适合应用在工业产品表面质量在线检测领域。

Lightweight defect detection algorithm based on multi model cascade

Defect detection algorithms based on deep learning technology often need a large number of image samples to train the model because of many network parameters. However, in the process of industrial production, the number of defective products is very small, and collecting a large number of defect data images is time-consuming and laborious. To solve this problem, this paper proposes a lightweight defect detection algorithm based on multi model cascade. It adopts the training method of supervised learning, and can obtain better detection results through a small number of defect samples. Firstly, CBAM attention residual module is used to extract features instead of conventional convolution layer to focus on defect features and strengthen the characterization ability of network to defects; Secondly, the SE-FPN module is designed to promote the effective integration of features at all levels and improve the segmentation effect of network on defects, especially for small defects; Finally, in the training stage, the supervised learning method is used to train the multi model algorithm network proposed in this paper. The experimental results show that the detection accuracy of the proposed algorithm on KolektorSDD data set is as high as 99.28%, and the average detection time of each image is only 10.5ms. It not only fully meets the requirements of high precision and real-time in the industrial detection industry, but also realizes the accurate positioning of defect areas. Therefore, the research content of this paper is very suitable for application in the field of on-line detection of surface quality of industrial products.