针对货车运行故障动态图像检测，提出无故障目标识别工作模式，解决货车枕簧丢失故障的自动识别问题。利用Haar特征提取枕簧特征信息，基于AdaBoost算法选取特征并构建层叠分类器，等比缩放搜索窗口检测货车图像，最终分选出无故障的枕簧图像，从而大大地减少了待识别图像的数量，显著地提高了人工识别效率。实验表明，该算法使用的特征简单，搜索策略高效，不受枕簧位置、缩放和旋转的影响，抗噪能力强，对分辨率低、局部遮挡、光照不足或过度曝光等质量较差的图像仍具有很强的适应性，所提出的方案能够满足全天候条件下的货车枕簧目标识别，为货车故障动态图像检测的工程化应用奠定了基础。

A novel conception of automatic recognition in the way of no trouble is proposed for trouble of moving freight car detection system (TFDS),to solve the detection problem of sleeper springs in freight cars. The recognition system inspires the feature pool of sleeper springs by Haar features,selects features by Adaboost algorithm,builds a cascade of classifier,scans the whole image with detector of the classifier at every scale,and at last separates images with no trouble. It drastically reduces the amount of detected images and improves the manual recognition efficiency. Experiments show that the proposed method applies a set of simple features and an efficiency detecting strategy,performs high robustness against noise as well as transformation,rotation and scale of objects,and indicates high stability to the images with poor quality,such as low resolution,occlusion,poor illumination and excess exposure etc. The method can recognize sleeper springs in all-weather conditions,which advances the engineering application for TFDS.