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Mutli-level Regression Anchor-free Object Detection

Recently, in the field of object detection, the anchor-free model has received widespread attention. It is based on keypoints to perform prediction and regression. Compared with the anchor-based model, it reduces the computational complexity and no additional hyperparameters are introduced. But the anchor-free approach faces the challenge of a large number of incorrect object regression boxes and the imbalance of the number of positive samples among objects in different sizes. To solve this, we present an efficient object detection algorithm named MRAF. We build our framework upon a representative anchor-free detector named FCOS. MRAF consists of two levels of detection network. The first network filters out most of the sample negative samples and regresses a two-dimensional vector for each sample point to roughly adjust the location. The second network performs classification and regression tasks based on the results of the first network to obtain the final detection results. Our approach greatly improves the accuracy of detection and the quality of the regression box.