深度卷积网络压缩算法在焊缝识别中的应用

Application of Deep Convolution Network Compression Algorithm in Weld Recognition

传统图像识别算法识别模型单一且易受外部光照条件干扰,深度卷积网络模型虽然识别率高,但计算量大,设备成本高,因此提出基于深度同或卷积网络的改进型压缩算法。首先介绍了焊缝识别系统的组成和经典卷积神经网络模型,然后阐述了改进型的卷积网络压缩算法,包括权值更新算法和权值补偿算法,最后在自制数据集和仿真平台上进行了数据实验。研究结果表明,所提算法具有识别率高、模型小、适应性强和识别模型多样化的优点,可应用于焊接现场对焊缝中心的识别。

The traditional image recognition algorithm has only a single recognition model and is susceptible to the external illumination interference. In contrast, as for the deep convolutional network model, there exist a large amount of calculation and high cost although its recognition rate is high. An improved based compression algorithm is proposed based on the deep XNOR-network. The compositions of the weld recognition system and the classical convolution neural network model are first introduced. The improved convolution network compression algorithm is described, including the weight update algorithm and the weight compensation algorithm. The data experiments are performed on the self-made datasets and the simulation platform. The research results show that the proposed algorithm has the advantages of high recognition rate, small model, strong adaptability and diversity of recognition models, which can be applied to the weld identification in the welding site.