关键词: 车辆检测, 卷积神经网络, 残差学习, 特征金字塔, 网络裁剪

Abstract:

In order to solve the problem of insufficient detection performance for vehicles with large scale transformation and overlapping, this paper proposes a single-stage deep convolutional neural network for real-time vehicle detection. For the scale-sensitive problems of vehicle detection algorithms, the deep residual network is used as the feature extraction layer to obtain fine-grained features, and the feature pyramid network is constructed for multi-scale detection; the soft non-maximum suppression is used to linearly attenuate the confidence score to solve the problem of vehicle overlapping so that the vehicle’s missed detection rate can be reduced. At the same time, it pruns the model at channel level to reduce the size of model parameters, saves computing resources and improves the speed of model detection. Experiments on VOC dataset show that the proposed method has high performance in both detection accuracy and detection speed. In terms of detection accuracy, it reaches 87.6%, which is 3.7 percentage points higher than that of YOLOv3, and 9.8 percentage points higher than that of SSD512. In terms of detection speed, the number of detection frames per second reaches 42 f/s, realizing real-time detection of vehicles. In particular, applying the model to the Apollo dataset with complex environment, it has better robustness than YOLOv3.

Key words: vehicle detection, convolutional neural network, residual network, feature pyramid network, network pruning