## 1 Introduction  
As a fundamental and challenging branch of computer vision, object detection aims to predict a set of boxes with categories for all instance in an image [1, 2, 3]. The pre-existing domain-specific image object detectors can be divided into two categories: one is anchor-based detectors, such as Faster R-CNN [4], YOLOv2 [5], YOLOv3 [6], SSD [7], RetinaNet [8], etc. ; and the other is anchor-free detectors, including YOLOv1 [9], DenseBox [10], Unitbox [11], CornerNet [12], FSAF [13], CenterNet [14], FoveaBox [15], etc. Anchor-based detectors usually tile a set of pre-defined anchors on the image, set as reference boxes for all objects. The scales and aspect ratios of anchors are obtained by clustering or manual design for a specific dataset. The detectors first assign anchors to different ground-truth bounding boxes based on the traditional intersection-over-union (IoU) sample selection strategy in training, followed by predicting the category and refining the coordinates of these anchors one or several times. The detectors decode regression offsets with corresponding anchors during testing and produce refined anchors as detection results. The anchor-based detectors still achieve state-of-the-art detection performance at this time. As mentioned in previous works [16, 17, 18], the pre-defined anchor strategy has some flaws: (1) the anchor boxes involve a considerable number of calculations, such as calculating IoU scores with ground-truth boxes; (2) hyper-parameters, including the scales, aspect ratios and number of anchors, are related to the dataset. Hyper-parameters also need to be redesigned for different tasks, which limits the generalization ability of the algorithm. Meanwhile, these hyper-parameters severely affect the detector's performance;(3) as anchors' scales and aspect ratios are fixed, detectors encounter difficulties when detecting objects with significant shape variations. To mitigate the above problems of the pre-defined anchor scheme, we propose a more straightforward and more effective method to generate anchor boxes. Our approach is inspired by the fact that anchor-based detectors usually use an IoU threshold to select the positive anchors. As shown in Figure 1, we show a group of anchor boxes. For each point on the feature map, the pre-defined anchor strategy sets three sizes of anchor boxes, and the aspect ratios of anchors are also set to three types, including 1:1, 1:2, and 2:1. Therefore, nine anchor boxes are placed on each feature point. For an image with a size of 608 \(\times\) 608, the number of pre-defined anchors has reached an amazing 69K. In the training, by setting the threshold (usually 0.4 and 0.5) and calculating the intersection-over-union scores between anchor boxes and ground-truth boxes, we assign anchors to each ground-truth box. Most of the anchors will not match the ground-truth box, as their IoU scores are lower than 0.4, as shown in Figure 1c. Then, a large number of negative anchors participate in classification loss calculation, which will cause a serious imbalance between the positive and negative samples. In the heads of detectors, the model will regress the offsets of the matched anchor box and the ground-truth box, and anchors with higher scores are easier to regress and obtain more accurate predictions. Therefore, improving the IoU scores of the anchor boxes can obtain more positive candidate boxes and lead to more accessible box regression. At the same time, reducing the number of anchor boxes is conducive to improving reasoning speed and reducing the requirements of computing resources. As the optimal anchor box of an object is related to its features, the detectors attempt to predict the shapes of anchor boxes with feature maps.