3D car-detection based on a Mobile Deep Sensor Fusion Model and real-scene applications

Unmanned vehicles need to make a comprehensive perception of the surrounding environmental information during driving. Perception of automotive information is of significance. In the field of automotive perception, the sterevision of car-detection plays a vital role and sterevision can calculate the length, width, and height of a car, making the car more specific. However, under the existing technology, it is impossible to obtain accurate detection in a complex environment by relying on a single sensor. Therefore, it is particularly important to study the complex sensing technology based on multi-sensor fusion. Recently, with the development of deep learning in the field of vision, a mobile sensor-fusion method based on deep learning is proposed and applied in this paper——Mobile Deep Sensor Fusion Model (MDSFM). The content of this article is as follows. It does a data processing that projects 3D data to 2D data, which can form a dataset suitable for the model, thereby training data more efficiently. In the modules of LiDAR, it uses a revised squeezeNet structure to lighten the model and reduce parameters. In the modules of cameras, it uses the improved design of detecting module in R-CNN with a Mobile Spatial Attention Module (MSAM). In the fused part, it uses a dual-view deep fusing structure. And then it selects images from the KITTI’s datasets for validation to test this model. Compared with other recognized methods, it shows that our model has a fairly good performance. Finally, it implements a ROS program on the experimental car and our model is in good condition. The result shows that it can improve performance of detecting easy cars significantly through MDSFM. It increases the quality of the detected data and improves the generalized ability of car-detection model. It improves contextual relevance and preserves background information. It remains stable in driverless environments. It is applied in the realistic scenario and proves that the model has a good practical value.