关键词: 车辆检测, 小目标, 遮挡目标, Cascade RCNN, ROI Align

Abstract:

Aiming at the problem of misdetection and missed detection of small targets and occlusion targets in the process of vehicle target detection, an improved Cascade RCNN vehicle target detection method is proposed. Firstly, the improved feature pyramid is used to integrate the shallow information into the deep network layer by layer to enhance the small target and occlusion target features. Then the multi-branch dilated convolution is introduced to reduce the feature loss during the downsampled process. Finally, the region of interest and context information through ROI Align unify size and fuse to enhance the target feature expression. Experimental results show that the improved Cascade RCNN can better detect small targets and occlusion targets, which is 2.2 percentage points and 2.7 percentage points  higher than that of Cascade RCNN on the KITTI and UA-DETRAC data sets.

Key words: vehicle detection, small targets, occlusion targets, Cascade RCNN, ROI Align