Research on vehicle detection algorithm based on convolutional neural network and combining color and depth images
Dong Wang, Zhou Yang, Ling Wu, Zhang Yonghui, Ting Li, Qiao Xiaoliang · 2019
In order to ensure the safety of driving, this paper studies the vehicle detection algorithm based on convolutional neural network which integrates color and depth images. This paper mainly discusses the subject through convolutional network multi-scale forward looking depth imaging positioning model, forward looking variable scale vehicle detection pre-positioning algorithm, and typical model identification algorithm based on transfer learning, and verifies the algorithm performance and model identification algorithm accuracy by combining with simulation test. The results show that the accuracy of the algorithm is 96.89% under different weather conditions, indicating its effectiveness in application. Compared with previous vehicle detection algorithms, the detection accuracy of this algorithm is effectively improved by 3.96%, indicating that this algorithm has innovative significance.