Computer Engineering and Applications ›› 2019, Vol. 55 ›› Issue (8): 182-188.

### Non-Motor Vehicle Target Detection Based on Deep Learning

LU Xue, LIU Kun, CHENG Yongxiang

1. College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
• Online:2019-04-15 Published:2019-04-15

### 一种深度学习的非机动车辆目标检测算法

1. 上海海事大学 信息工程学院，上海 201306

Abstract: In order to solve the problem of target detection in road traffic scenes, this paper proposes a target detection method based on EdgeBoxes algorithm and deep learning fusion. Using the Fast R-CNN, a deep learning target classification algorithm, combined with VOC format non-motor vehicle data samples, the problem of target detection is factored into the classification of bicycle and evbike. It uses EdgeBoxes algorithm to extract the object proposals of the samples, then to build moderate regions of interest and enter the network together with the sample for iterative training. At the same time, regularization and fine tuning strategies are introduced to optimize the network, reducing network complexity and avoiding over-fitting. After network training the non-motor vehicle target detection model is obtained, a new sample test is performed on the model and the test result is analyzed. Compared with the traditional method, the detection method based on EdgeBoxes algorithm and optimized Fast R-CNN method improves the detection accuracy slightly in road traffic scenes detection, and the computational complexity is significantly reduced and the detection speed is nearly doubled.