A method for detecting an obstacle, comprising: detecting, by a millimeter-wave radar, position points of candidate obstacles in front of a vehicle; detecting, by a camera, a left road boundary line and a right road boundary line of a road on which a vehicle is located; separating, the position points of the candidate obstacles according to the left road boundary line and the right road boundary line of the road on which the vehicle is located, and extracting position points between the left road boundary line and the right road boundary line; projecting, the position points between the left road boundary line and the right road boundary line onto an image; acquiring, on the image, regions of interest, ROIs, corresponding to the projection points, wherein the ROIs corresponding to the projection points are regions of regular shapes centered on the position points; calculating detection results of the ROIs corresponding to the projection points through a deep convolutional network, wherein a detection result comprises: a detection result indicting existence of an obstacle or a detection result indicting non-existence of an obstacle; and detecting the target obstacle in front of the vehicle according to the detection results of the ROIs corresponding to the projection points, wherein after the calculating the detection results of the ROIs corresponding to the projection points through the deep convolutional network, and before the detecting the target obstacle in front of the vehicle according to the detection results of the ROIs corresponding to the projection points, the method further comprises: extracting from the image, based on pre-determined position points falsely-detected by the radar, projection points of the falsely-detected position points on the image; filtering out, in the detection results of the ROIs corresponding to the projection points outputted by the deep convolutional network, the projection points of the falsely-detected position points and detection results of ROIs corresponding to the projection points of the falsely-detected position points; and determining the obstacle in front of the vehicle based on detection results of ROIs corresponding to projection points after the filtration. detecting, by a millimeter-wave radar, position points of candidate obstacles in front of a vehicle; detecting, by a camera, a left road boundary line and a right road boundary line of a road on which a vehicle is located; separating, the position points of the candidate obstacles according to the left road boundary line and the right road boundary line of the road on which the vehicle is located, and extracting position points between the left road boundary line and the right road boundary line; projecting, the position points between the left road boundary line and the right road boundary line onto an image; acquiring, on the image, regions of interest, ROIs, corresponding to the projection points, wherein the ROIs corresponding to the projection points are regions of regular shapes centered on the position points; calculating detection results of the ROIs corresponding to the projection points through a deep convolutional network, wherein a detection result comprises: a detection result indicting existence of an obstacle or a detection result indicting non-existence of an obstacle; and detecting the target obstacle in front of the vehicle according to the detection results of the ROIs corresponding to the projection points, wherein after the calculating the detection results of the ROIs corresponding to the projection points through the deep convolutional network, and before the detecting the target obstacle in front of the vehicle according to the detection results of the ROIs corresponding to the projection points, the method further comprises: extracting from the image, based on pre-determined position points falsely-detected by the radar, projection points of the falsely-detected position points on the image; filtering out, in the detection results of the ROIs corresponding to the projection points outputted by the deep convolutional network, the projection points of the falsely-detected position points and detection results of ROIs corresponding to the projection points of the falsely-detected position points; and determining the obstacle in front of the vehicle based on detection results of ROIs corresponding to projection points after the filtration. extracting from the image, based on pre-determined position points falsely-detected by the radar, projection points of the falsely-detected position points on the image; filtering out, in the detection results of the ROIs corresponding to the projection points outputted by the deep convolutional network, the projection points of the falsely-detected position points and detection results of ROIs corresponding to the projection points of the falsely-detected position points; and determining the obstacle in front of the vehicle based on detection results of ROIs corresponding to projection points after the filtration.