关键词: 目标检测, EdgeBoxes, 深度学习, Fast R-CNN

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.

Key words: target detection, EdgeBoxes, deep learning, Fast R-CNN