关键词: 目标检测, 对抗学习, 深度估计, 　3D 场景稀疏重建

Abstract: With the continuous development of target detection technology,the vehicle detection system for road scenes has been widely used in the field of automatic driving. Compared with the traditional target detector, though the target of vehicle detection is relatively simple, two major problems need to be solved. First, the characteristics which provided to the detector are usually incomplete in some complex road scenes, and other problems such as occlusion and deformation will occur. Second, it is necessary to estimate the distance of different vehicles to ensure the car can make timely evasive action in the process of automatic driving, which means it needs depth estimation of the target area of the image. Aiming at these two problems, a vehicle detection system based on anti⁃sample generation and depth map reconstruction was proposed. A confrontation network was designed for the pre⁃training target detection network called Faster⁃RCNN, which was used to generate a large number of samples during the training process, and train the vehicle detector with these samples. According to the detection results, the vehicle distance is estimated to inform the system to make evasive action in time through the reconstruction of 3D scene and camera pose recovery depth map. The experimental results show that this detection system can improve the detection effect and estimate the distance of the target vehicle without increasing the data training sample.

Key words: Object detection, Adversarial learning, Depth estimation, 3D scene sparse recovery