随着火炮实弹射击发数的增加，身管内膛由于不断遭受高温、高压火药气体和弹丸的摩擦作用而产生多种疵病，影响射击精度和射击安全性。分析了内膛压痕、锈蚀和烧蚀等疵病边缘梯度和边缘方向上的特点，将疵病图像分为遍布性疵病和区域性疵病两大类；根据图像Radon变换对方向的敏感性和Susan边缘检测算子便于提取含有梯度信息的边缘特点，定义了遍布性疵病和区域性疵病的分类因子，为后期疵病特征提取和识别提供了依据。实验证明，该算法能有效地对两类内膛疵病进行区分和计算，并且识别准确率高，从而实现了内膛疵病图像的定量预分类。

With the increase of ball firing times, multiple flaws will appear in the gun bore, due to the impact of high temperature-high pressure powder gas and the attrition of pills, which influences the fire accuracy and the fire safety. Through analyzing the gradient and direction characteristics of indentation, rust corrosion and burning corrosion, the gun bore flaw images are separated into spread flaw images and regional flaw images. Then the classification factor of the spread flaw image and the regional flaw image is defined according to the sensitivity to direction of Radon image transformation and the edge gradient of Susan operation. This classification offers basis for the following analysis. Experimental results show that the algorithm could classify the gun bore flaw image accurately through computing the flaw characteristics and thus realize the quantitative classification.