Nighttime three-dimensional target detection of driverless vehicles based on multi-view channel fusion network

In order to improve the ability of driverless vehicles to identify objects in the surrounding environment at night, a three-dimensional target detection method for driverless vehicles based on multi-view channel fusion network was proposed. The idea of multi-sensor fusion was adopted, and the target detection was carried out by adding laser radar point cloud on the basis of infrared image. By encoding the laser radar point cloud into a bird’s-eye view form and a front view form, as well as forming a multi-view channel with the infrared night vision image, the information of each channel was fused and complemented, thereby improving the ability of the driverless vehicle to recognize surrounding objects at night. The infrared image and the laser radar point cloud were used as the input of the network. The network accurately detected the position of the target and the category by the feature extraction layer, the regional suggestion layer and the channel fusion layer. The method can improve the object recognition ability of driverless vehicles at night, the accuracy rate in the laboratory test data reaches 90%, and the time consumption of 0.43 fps also basically meets the practical application requirements.