A Real-Time Forward Collision Warning Technique Incorporating Detection and Depth Estimation Networks

The visual perception is of great significance for advanced driving assistance systems or autonomous driving vehicles to recognize the surrounding scenes. In the adaptation to the real environments for collision warnings, a sensor system should be efficient and has the strong ability to detect small objects. This paper presents a forward collision warning technique which incorporates the object detection and depth estimation networks. A deep convolutional neural network is constructed with transfer connection blocks for object detection and classification. It is capable of small object detection under the real-time processing requirement. For depth estimation, a monocular based disparity estimation network is adopted to the stereo vision framework. The epipolar constraint is applied to increase the prediction accuracy. In the experiments, the performance evaluation is carried out on public driving datasets. The comparison with the state-of-the-art networks has demonstrated the feasibility of the proposed technique.