Automatic road crack detection using image/video data plays a crucial role in the maintenance of road service life and the improvement of driving experiences. In this paper, an improved automatic road crack detection system is proposed to reduce false detection under various noisy road surface conditions and to improve sensitivity in detecting light and thin cracks. The proposed system combines a variety of traditional image processing techniques, such as filtering and morphological processing, with scalable and efficient machine learning algorithms. Real road images with various noise conditions are taken to evaluate the performance of the proposed system. Experimental results have shown that the proposed system improved detection sensitivity and reduced false detection compared to some existing system, thus achieving higher detection accuracy.