关键词: 深度学习, 交通场景, 车辆检测, 深度残差网络

Abstract: At present, in the field of intelligent transportation, vehicle target detection has become a research hotspot by using deep learning method. As traditional machine learning method is easily affected by external factors such as illumination, angle and image quality, and the cumbersome detection steps, current one-stage target detection models and two-stage target detection models are analyzed, and vehicle target detection method is proposed by on one-stage target detection model and is named RetinaNet, which uses deep residual network to acquire image features autonomously, integrates MobileNet network structure to accelerate the model and transforms the target detection problem in complex traffic scenarios into the three-category problem of vehicle type. KITTI data set is adopted to train and actual scenes images are applied to test. Experimental results show that the proposed method improves the MAP value by 2.2 percentage points in comparison with original RetinaNet model.

Key words: deep learning, traffic scenes, vehicle detection, deep residual network