目的 在列车运行安全性评价中，轮轨冲角是一关键性指标。列车运行过程中车体存在强烈振动，而且在基于应变片及传感器的冲角接触测量方法中存在许多弊端，例如丢失，损坏等问题，但冲角作为轮轨接触安全状态评价中的重要参数，对列车运行稳定性评价方面具有重要意义。为了避免接触测量方法中测量传感部件易损坏，丢失以及冲角值小等原因导致轮轨冲角检测难度大等问题，提出了一种新的轮轨冲角的图像检测方法。方法 首先，将CCD传感器安装在转向架上构成视觉检测系统；其次，根据相机物像空间几何成像模型建立车轮运动中轮缘角度与图像中椭圆短轴变化关系，实现对3维空间中轮缘位置和角度信息的采样与记录，并证明了轮缘几何特性与车轮偏转角度之间单一映射关系，将轮轨冲角φ的检测转化为椭圆短轴长度特征的检测，缩减了检测难度，增加了可行性；最后，给出冲角仿真结果。结果 实验结果表明，该方法测得的冲角值与使用仪器检测的冲角数值平均误差为0.024°，最大误差0.084°，单帧图像检测时间大约400 ms，通过仿真得出冲角的范围在0.75°内，且随机车速度的增大而增大，冲角在径向机构未锁定时小于锁定状态。结论 该方法检测速度较快且准确性较高，具有一定的工程应用价值，能为后续列车运行的稳定性和安全性评价奠定了一定的基础。

Objective Wheel-rail attack angle is a key index in the evaluation of locomotive operation safety. Strong vibration occurs during the train operation process, and many disadvantages in the attack angle measurement methods exist based on the strain gauge transducer and sensor in the contact measurement, such as loss, damage, and other problems. The attack angle, as an important parameter in safety evaluation and the evaluation of the running stability of trains, has great significance. A new method forimage detection of wheel-rail attack angle is proposed to avoid issues in contact detection, such as the loss and damage of sensors and detection difficulty due to the minimal value of the attack angle.Method This method uses a charge-coupled devicesensor installed on the train bogie to form a visual inspection system. The single mapping relationship between the geometry of the target image and the object deflects the attack angle of the wheel-rail. Thus, the parameter of the attackangle φ measurement of locomotive inspection into the detection of elliptic geometry includes the characteristic of the minor axis and the major axis. This parameter reduces the difficulty of wheel-rail attack angle detection and increases the feasibility of the measuring method, thereby ensuring easy image detection of the locomotive attack angle. Simulation results of the attack angle are given to prove measurement accuracy and availability.Result Experimental results show that the average error between the value of the attack angle measured by a measuring apparatus and theangle used by the proposed method is 0.024°. For the experimental data, the maximum error is 0.084°, and the simulation model of attack angle is established. The changes before the locking mechanism and after locking the radial angle are compared. The angle value is large when the locking mechanism is locked. The single frame image detection time is approximately 400ms, which is related to the velocity of the train; the result is highly accurate. Conclusion The proposed method detects the wheel-rail attack angle quickly and achieves a highly accurate result. Thus, this study establishes a foundation for the stability and safety evaluation of trains.