Abstract: The environment perception based on 3D detection is the basis of unmanned driving technology of trackless rubber-tyred vehicle in coal mine. Due to the lack of light in the underground environment, the RGB image information is missing, and the narrow roadway space leads to more noise in the point cloud data collected by laser radar, so the existing 3D target detection methods based on image or radar point cloud can not achieve good detection effect in the underground. In order to solve this problem, a 3D target detection method for unmanned trackless rubber-tyred vehicle is proposed, which fuses image and radar point cloud. The obtained driving environment data of the trackless rubber-tyred vehicle is preprocessed. The global histogram equalization method is used to improve the brightness of RGB images and reduce the effect of uneven lighting in coal mine. The bilateral filtering and denoising and principal component analysis dimensionality reduction processing are performed on radar point cloud data to improve the quality of point cloud data and reduce computing time. A fusion image and radar point cloud detection model is designed. The region proposal network is used to generate 2D image candidate regions, which are fused with the point cloud data at the early characteristic level to generate 3D candidate regions, and then fused with the pooled image and point cloud data at the later region level to output the 3D detection anchor frame to realize target detection. The experimental results show that compared with the detection methods based on YOLO3D and MV3D models, the proposed method has higher detection precision of the target to be tested, and achieves a better balance between precision and detection speed. The underground test results show that the method can accurately detect the position of pedestrians or vehicles in the driving environment of trackless rubber-tyred vehicle, and has good underground adaptability.