基于BP神经网络的枪弹外观缺陷识别与分类

Identification and classification of bullet surface defect based on BP neural network

为实现枪弹外观缺陷自动检测,提出一种基于BP神经网络的枪弹外观缺陷自动识别与分类方法。首先针对枪弹外观缺陷图像特点,从形状、颜色、纹理提取类别差异明显的缺陷特征向量,作为神经网络的输入,以提高分类效果;然后通过经验和实验验证确定神经网络结构及参数,并分析传统BP算法在枪弹外观缺陷分类应用中的不足,通过优化BP算法以提高网络分类性能。实验表明:优化BP算法能够有效分类枪弹外观缺陷测试样本,识别率达到92.1%,与传统BP算法相比,提高了收敛速度,并表现出较好的准确性和鲁棒性,能够更好满足枪弹外观缺陷自动检测要求。

In order to achieve automatic detection of bullet surface defect, a new method is proposed for autmuatically identifying and classifying the bullet surface defects on the basis of BP neural network. Firstly, according to the property of bullet surface defects, the distinct defect feature vector is extracted as the import of neural network from shape, color and texture. Secondly, the structure and parameter of neural network are ascertained by experience and experiment confirmation, the disadvantage of bullet surface defect classification by BP neural network is analyzed, and the classification capability of network is improved by optimized BP method. The experimental results show that the test stylebook of bullet surface defect can be classified by the optimized BP method effectively, and the discriminating rate can reach 92.1%. In tb~ experiment of contrast with traditional BP method, the speed of convergence is improved, and the new method has a good ability of accuracy and robustness, can better satisfy the need of automatic detection of bullet surface defects.