Abstract
A novel conception of automatic recognition for free-trouble sleeper springs is proposed and Adaboost algorithm based on Haar features is applied for the sleeper springs recognition in Trouble of moving Freight car Detection System (TFDS). In the recognition system, feature set of sleeper springs is determined by Haar features and selected by Adaboost algorithm. In order to recognize and select the free-trouble sleeper springs from all the captured dynamic images, a cascade of classifier is established by searching dynamic images. The amount of detected images is drastically reduced and the recognition efficiency is improved due to the conception of free-trouble recognition. Experiments show that the proposed method is characterized by simple feature, high efficiency and robustness. It performs high robustness against noise as well as translation, rotation and scale transformations of objects and indicates high stability to images with poor quality such as low resolution, partial occlusion, poor illumination and overexposure etc. The recognition time of a 640×480 image is about 16ms, and Correct Detection Rate is high up to about 97%, while Miss Detection Rate and Error Detection Rate are very low. The proposed method can recognize sleeper springs in all-weather conditions, which advances the engineering application for TFDS.
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