A lane detection system using around view monitoring (AVM) images is presented in this paper. To provide safe driving condition, many lane detection approaches have been proposed. However, previous approaches cannot detect lanes stably in low visibility condition such as foggy or rainy days because of the use of frontal camera. The proposed lane detection system uses ego-vehicle's surrounding road information to overcome this problem. The proposed method can be split into two stages: generation of AVM images from four fisheye cameras and lane detection using AVM images. To generate AVM images, we use four fisheye cameras mounted on sides, front, and rear of the vehicle. Top-view images covering the surround area of the vehicle are generated from four fisheye images by calibrations of each camera and their relative camera pose. The lane detection procedure consists of detecting and grouping lane responses, fitting lane responses by a linear model, and tracking lanes with kalman filter to smooth the estimates. Experimental results on full lanes and dashed lanes show that the proposed method can achieve the detection accuracies of 98.78% and 90.88% respectively and processing speed of 1 ms per frame in a desktop computer.