To solve the problem of spatial information loss in point cloud processing， and extract the texture information of visible images to the maximum extent during the fusion， a vehicle detection method based on laser point cloud and visible image fusion is proposed. The point cloud processing idea of front views based on the original information is incorporated into the CrossGAN-Detection method. The point cloud is projected to the front view angle， and each dimension of the original point cloud information is sliced into feature channels， significantly improving the utilization efficiency of the point cloud information without reducing network performance. The idea of relative probability is introduced， and the relative real probability， instead of the absolute real probability， of the discriminator is used to identify the image such that the texture information extracted is fused. The experimental results show that the AP indexes of this method in the three categories of easy， medium， and difficult of KITTI dataset are 97.67%， 87.86%， and 79.03% respectively. In a scene with limited light， the AP index reaches 88.49%， which is 2.37% higher than that of the CrossGAN-Detection method. Hence， target detection performance is improved.