Detection of Imaged Objects with Estimated Scales

Dealing with multiple sizes of the object in the image has always been a challenge in object detection. Predefined multi-size anchors are usually adopted to address this issue, but they can only accommodate a limited number of object scales and aspect ratios. To cover a wider multi-size variation, we propose a detection method that utilizes depth information to estimate the size of anchors. To be more specific, a general 3D shape is selected, for each class of objects, that represents different sizes of 2D bounding boxes in the image according to the corresponding object depths. Given these 2D bounding boxes, a neural network is used to classify them into different categories and do the regression to obtain more accurate 2D bounding boxes. The KITTI benchmark dataset is used to validate the proposed approach. Compared with the detection method using pre-defined anchors, the proposed method has achieved a significant improvement in detection accuracy.