# AspectNet: Aspect-Aware Anchor-Free Detector for Autonomous Driving
## 2 Related Work
### Anchor-Free Approaches  
Anchor-free approaches can directly detect objects without preset anchors and can be divided into two directions: key point-based methods and center-based methods. Most are single stage because both methods are dense detectors. Key point-based methods use multi-key point joint expression to predict objects. For example, ExtremeNet [15] uses the four extreme points and the center point to make predictions. RepPoints [16] adaptively learns a set of points to represent boxes. FoveaBox [17] uses the center point, and upper left and lower right corners. PLN uses the center point and four corner points. CornerNet uses a pair of corner points to regress the bounding anchor based on key point-based methods. However, it requires an extra distance metric to store the pair of corner points that belong to the same object, which requires complicated post-processing. Compared with key point-based methods, structure center-based methods are more concise and can achieve better performance [18]. Therefore, the proposed model AspectNet uses the center-based approach to detect objects. Center-based methods use the center point for fitting to predict objects. YOLOv1 [19] uses points near the object's center to regress the bounding box instead of using anchor boxes. However, YOLOv1 has the characteristics of high precision and low recall because it only uses a small number of positive samples to learn features, and was quickly replaced by the anchor-based detector YOLOv2 [20]. CenterNet models the bounding box regression problem as the problem of learning the center point and distance between the center point and the corresponding bounding box, and directly uses the center point regression method to predict objects without NMS for post-processing. The structure is simple and effective. However, CenterNet's positive samples are only defined as the center point of the ground truth, and the remaining points are negative samples. This simple approach to defining positive and negative samples causes CenterNet to have a severe imbalance of positive and negative samples, which also causes the overfitting of regression branches. FCOS is a pixel-wise object detection algorithm based on FCN [21], which alleviates the problem of positive and negative sample imbalance by adding a centeredness branch. It uses the feature pyramid network (FPN) for multi-scale prediction. FCOS defines the points in the bounding boxes of the object as positive samples, and theremaining points are negative samples. Compared with CenterNet, FCOS has a larger number of positive samples. However, FCOS does not define the ignored area of the sample, and the focal loss makes FCOS pay more attention to the noisy points around the bounding box. Additionally, the position of the bounding box edge is used as a positive sample for which it is difficult to obtain accurate results for prediction.  
The excessive attention paid to difficult negative samples makes the loss unstable and difficult to decrease, hence not robust. The proposed method alleviates this problem by redefining sample selection and redesigning the loss function. Additionally, the aspect ratio branch of the proposed method can make up for the lack of prior knowledge of the aspect ratio in the anchor-free method.