基于卷积神经网络的焊接装配特征识别研究

为实现高铁白车身焊接拼装技术的智能化与自动化,解决焊接过程中特征区域小、背景干扰多等问题,提出了基于迁移学习和卷积神经网络的焊接装配特征快速识别算法。首先采用二值化等传统图像处理算法确定待提取特征的粗略位置,在此基础上再使用sobel、腐蚀、霍夫线段检测确定特征区域的精确位置。其次,考虑到不同环境下,精确定位后特征区域表现不同,故采用基于卷积神经网络的分类模型以增强预测模型的鲁棒性和准确性。最后,选择基于迁移学习的的视觉几何群网络(VGG16)来解决样本量不足以训练整个模型参数的问题。实验结果表明,本文所提的识别算法能够准确识别型材的状态,且在识别检测速度上优于YOLOV3,在准确率上劣于YOLOV3,算法满足使用场景下的实时性要求。

The research on high speed underwater target recognition based on fuzzy logic inference

The underwater target recognition is a key technology in acoustic confrontation and underwater defence. In this article, a recognition system based on fuzzy logic inference (FLI) is set up. This system is mainly composed of three parts: the fuzzy input module, the fuzzy logic inference module with a set of inference rules and the de-fuzzy output module. The inference result shows the recognition system is effective in most conditions.