Abstract: Object detection is an active research field in the computer vision field.It is a very effective method to improve object detection precision by designing a large-scale deep convolutional neural network.However,it is unfavorable to deploy a large-scale object detection network in memory-limited applications.To solve the above problems,this paper proposes a light-weight object detection network which is based on design principles from the YOLO family of single-shot object detection network architectures.This network integrates the Ghost Module in GhostNet,in addition,a better Efficient Channel Attention (ECA) module is added to the convolution block by referring to the Squeeze-and-Excitation (SE) module in MobileNet-v3.This module can make better use of the available network capacity,making the network achieve a strong balance between reducing the complexity of architecture and computation and improving the performance of the model.In addition,Distance-IoU loss is used to solve the problem of inaccurate regression position of bounding box and effectively speeds up network convergence.Finally,the number of parameters of the model was compressed to 1.54 MB less than YOLO Nano (4.0MB),and the mAP on the VOC2007 data set was 72.1% higher than the existing YOLO Nano (69.1%).

Key words: Light-weight, Object detection, Pascal VOC, YOLO deep convolutional neural network