# An Enhanced Feature Pyramid Object Detection Network for Autonomous Driving
## 1 Introduction  
Autonomous driving systems cognize the surrounding environment by modeling the street scenery information acquired by sensors, and then makes driving decisions. Among them, object detection plays an important role in the construction of realistic scenes. In recent years, the improvement of deep learning theory and computing power has significantly accelerated the development of object detection. Nowadays, object detection methods are divided into two branches, one-stage and two-stage. Two-stage algorithms are widely applied in tasks with a high degree of accuracy. A typical two-stage deep learning-based detection framework includes a feature extraction subnet, a proposal extraction subnet, and a detection subnet. The feature extraction subnet [1; 2; 3] uses a convolutional neural network to extract features with high robustness and rich information through end-to-end training process. The proposal extraction subnet [4; 5] generates regions of interest (Rols), including foreground positive samples and background negative samples from the feature map. The detection subnet utilizes the pooling feature of RoIs to predict the classification and regression results of detected objects [5; 6; 7; 8; 9]. To utilize the features obtained from the feature extraction subnet, initially, researchers feed the single scale features from the last convolution layer into the subsequent subnet [5; 7; 8]. Although these features are rich in higher-level semantic information, they lack detailed information. This leads to apoor detection performance for small objects and occluded objects. Two kinds of network structures are proposed to solve this problem. One is to do prediction separately on feature layers of different resolutions [10; 11]. The other is to merge multi-resolution features at first and then to predict on the merged feature map [12; 13]. Further studies show that taking advantages of both structures can get more accurate results [14; 15; 16; 17]. FPN is a state-of-the-art network of such structure. FPN constructs a feature pyramid with high-level semantics throughout and independently predicts at each pyramid level. The key design for building feature maps for each pyramid level is the lateral connection. It merges the semantically stronger feature maps from top-down with feature maps which are rich in detail localization information from the same bottom-up level. However, feature maps within the same pyramid level have the same weight for subsequent object detection, which ignores the feature requirements of different scale objects. Inspired by the features strengthen block in image classification [18] and segmentation [19] field, we propose a generic FWM to recalibrate feature maps in each pyramid level. For each pyramid layer, learnable weights are applied between features along space and channel to boost useful features and suppress useless features specific for different scale detection requirements. After that, a new enhanced feature pyramid is reconstructed with more powerful feature representation. After calculating the shared features, the proposal extraction subnet generates RoIs. Then, the detection subnet adopts RoI pooling to map and extract the corresponding fixed-size features of RoIs for classification and regression. However, for small and occluded objects whose feature information is limited by their size or physical occlusion, their RoI features are less informative for subsequent prediction. Researchers argue that using image evidence beyond RoI content benefits object detection [10; 13; 20; 21; 22; 23] and segmentation [21]. Such context information can be integrated by a spatial recurrent neural network or acquired by expanding or deforming original RoI regions. We mainly focus on local context embedding methods by expanding RoI regions. [PERSON] et al. [20] defined ten kinds of context regions around RoI to enrich the RoI representation, and then integrated each region's information in separate fully connected layers and concatenated them together as final features of each RoI.