Binary residual feature pyramid network: An improved feature fusion module based on double‐channel residual pyramid structure for autonomous detection algorithm

Abstract

Abstract The vehicle detection algorithm based on visual perception has been applied in all types of automatic driving scenes. However, there are still flaws in the current detection algorithm model, especially for small objects. The detection effect of vehicle objects with small pixels in the image is often missed and wrongly detected. This research proposes an improved feature fusion module based on double‐channel residual pyramid (DRP) structure for autonomous detection algorithm which named binary residual feature pyramid network (BiResFPN) to solve the above problems. Firstly, a DRP structure, which can effectively supplement the shallow information of the network, is proposed. The residual structure is added to the output feature map for further supplement. Then, an average sampling method of positive and negative samples based on intersection‐over‐union (IOU) value is proposed on the basis of this structure, aimed at the unbalanced sampling of positive and negative samples in the training stage of faster regions with CNN features (RCNN). It leads to the reduction of the interference of a large number of simple negative samples, which makes the learned model better. The experimental results based on the KITTI and BDD100K dataset datasets show that the capability of the feature fusion module based on DRP structure is strong for small object detection. Compared with Faster‐RCNN (FPN), the detection algorithm of small object detection accuracy APsmall was increased by 2.6%, APmedium and APlarge was increased by 1.1% and 0.3%.