This paper proposes a method using exploring agent for the noise elimination and crack recognition in binary images which originate from the objective gray level images. A mean filtering method is introduced to correct non-uniform background illumination and obtain the dynamic thresholds, which are used to convert the original 255 scales gray level image into binary images. The pavement crack figures in the binary image have been contaminated by randomly distributed noisy dots, and in most cases, the crack shape and orientation can't be represented by specific functions. The exploring agent method using sense-compute-act loop, presented in this paper, can be employed to determine the crack and eliminate the random noise. The exploring agent and the Least Square Fit (LSF) method separately have unique characteristics in recognizing the crack intersection and orientation, and automatically running along the crack. The traces of the exploring agent are the skeleton of the pavement crack, and the number of steps can be used to calculate the length of the crack. The sense, compute, and act ability of the exploring agent iterate to guarantee the effect in processing randomly distributed features of image during the actual processing.