为了提高基于双目视觉的三维目标检测的精度与鲁棒性,提出了一种基于迭代式自主学习的三维目标检测算法。首先,为了给三维目标检测任务提供更精准的目标点云信息,提出了一种基于迭代式自主学习的视差估计算法,通过迭代地增加目标区域的视差监督信号以及引入选择性优化策略,提高了视差估计在目标区域中的准确性。其次,在网络结构中,提出了一种自适应特征融合机制,将不同模态信息的特征进行自适应融合,进而得到准确且稳定的目标检测结果。结果表明,与近年来较流行的基于视觉系统的算法相比,所提出的三维目标检测算法在检测精度上有较大提升。

To improve the precision and robustness of 3D object detection based on stereo vision, a novel 3D object detection algorithm based on iterative self-training is proposed. To acquire the precise object point clouds for 3D object detection task, a disparity estimation algorithm based on iterative self-training is first proposed, which is capable of improving the disparity accuracy of object region by increasing the supervised signal in object region iteratively and introducing a selective optimization strategy. Then a self-adaptive feature fusion mechanism is proposed in network architecture, which adaptively fuses the features from multimodal information to obtain the precise and robust object detection results. Compared with the recent and popular algorithms based on vision system, the proposed 3D object detection algorithm achieves a great improvement in precision.