Abstract: Recent advances in convolutional neural networks(CNNs) have led to significant improvement in object detection.To solve the problem of missing context and multi-scale information of SqueezeDet algorithm,this paper combines skip connection and shortcut connection to aggregate multi-scale feature maps,and use dilated convolution to expand the convolutional receptive field and context.A context-based multi-scale object detection model was proposed to effectively improve the accuracy and robustness of object detection for complex scenes.This model fuses three different resolution feature maps:the minimum and middle size feature maps gather context through dilated convolution,the minimum size feature maps are doubled through bilinear interpolation and the maximum size feature maps use convolution whose stride is 2 to down-sample.Then the three feature maps have the same size and can be fused.In addition,this paper uses shortcut connection to connect different size of feature maps to obtain lost information from the larger feature maps.The model is evaluated on the autopilot international benchmark dataset KITTI and achieves 6% improvement compare to the SqueezeDet.The speed of the model reach 30fps on a GPU.

Key words: Convolutional neural network, Dilated convolution, Shortcut connection, Skip connection