关键词: 深度学习, 卷积神经网络, 目标检测, RefineDet算法, 感受野模块（RFB）, 轻量级特征化的图像金字塔（LFIP）, 参数化修正线性单元（PReLU）, 损失函数, 遮挡目标

Abstract:

Aiming at the problem of low detection accuracy of convolutional neural network model for object detection in driving vision, a multi-scale feature fusion object detection method based on improved RefineDet is proposed. Firstly, the LFIP（Light-weight Featured Image Pyramid） network is embedded in the RefineDet, and the multi-scale feature map generated by LFIP network is integrated with the main feature map output from ARM（Anchor Refinement Module） in the RefineDet, which improves the output effect of anchors preliminary classification and regression in the convolutional layer, and provides refined anchors frame for ODM（Object Detection Module） for further regression and multi-class prediction. Secondly, after the ODM in the RefineDet, a multi-branch structure RFB（Receptive Field Block） is embedded to obtain receptive fields of different scale in the detection task to improve the features extracted from the backbone network. Thirdly, the activation function in the model is replaced by the nonlinear activation function PReLU（Parametric Rectified Linear Unit） with learnable parameters to speed up the convergence of the model. Then, the Bounding box loss function of RefineDet is replaced by the Repulsion Loss function to narrow the gap between a proposal and its designated target and increase the distance between the proposal and the surrounding non-target objects. Finally, an object detection dataset is constructed with 48 260 images in driving vision, including 38 608 as training set and 9 652 as test set, which are verified on mainstream GPU hardware platform. The mAP of this method is 85.59%, which is better than RefineDet and other improved algorithms;the FPS is 41.7 frame/s, which meets the application requirements of driving scene object detection. Experimental results show that the proposed method can improve the accuracy of object detection in driving vision, and solve the problems of occlusion object detection and small object detectionin driving vision to a certain extent.

Key words: deep learning, convolutional neural network, object detection, RefineDet algorithm, Receptive Field Block（RFB）, Light-weight Featured Image Pyramid（LFIP）, Parametric Rectified Linear Unit（PReLU）, loss function, occlusion object