Abstract: The current object detection model has disadvantages like large number of parameters,large size and slow detection speed,and it cannot be applied in real-time scenarios.For example,automatic driving technology requires not only accurate detection to ensure safety,but also rapid detection to ensure real-time decision-making of vehicles.For the above questions,this paper presented an end-to-end light-weight object detection network FGHDet.Firstly,grouping heterogeneous convolution(GHConv) is proposed to solve the problem of low efficiency of HetConv channel by channel convolution.Secondly,the basic module FGH Module is built by combining GHConv and Fire Module.Finally,the end-to-end light-weight object detection network FGHDet is built based on FGH Module.FGHDet reduces the amount of parameters mainly in two ways.One is to reduce the number of input channels of the 3×3 filter,and other is to replace the traditional convolution kernel with GHConv.This paper took KITTI data set as experimental data to complete the training and evaluation of the model on the deep learning framework Keras.The experimental results indicate that the mAP of FGHDet in KITTI data set can reach 74.4%,higher than 70.8% of Faster R-CNN,and the model detection speed is 28.7FPS,better than SqueezeDet,the fastest model in the comparison models.Moreover,the size of the proposed model is only 2.6MB,1/200 times the volume of the Faster R-CNN model.

Key words: FGHDet, Grouping heterogeneous convolution, KITTI, Light-weight, Object detection