Obstacles detection is one of the most important parts for ADAS (Advanced Driver Assistance Systems). Camera provides excellent recognition but with limits to range information; nevertheless, the LiDAR allows for better range information but with limits to the object identification. This paper deals with the problem of efficiently and accurately detecting vehicles on-load by fusing color images and LiDAR point clouds. Firstly, a neural network is used to detect road and vehicles. This neural network has high accuracy and speed on detection for the encoder in it is shared by different tasks. In the second step, the point clouds are processed to remove some invalid points and positions that potential represent targets generate by clustering point clouds. Positions are projected to images plane to get the ROI (Region of Interest), then the ROI will be matched with detection results of image to check if any targets are missed. In the paper, we adopt the RANSAC (Random Sample Consensus) algorithm to remove ground points. A parameter adaptive DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm is proposed to cluster points, where parameters can change adaptively according to the characteristics of different density point clouds. Through neural network, we recognize the types of obstacles. Experiment is performed on KITTI dataset, using left color images and Velodyne64 point clouds to verify our method. The result shows satisfactory accuracy in detection work.