关键词: 目标识别, 轻量化, 剪枝

Abstract:

In view of the current mainstream deep neural network model, which aims to improve the accuracy and ignores the real-time performance and the size of the model, a lightweight target recognition deep neural network is proposed. Based on the high-efficiency convolution method such as depth separation convolution and packet convolution, an invariant resolution convolution module and a downsampling module for image feature extraction are designed. Based on this, a deep backbone network is constructed and the branch is reduced. The target recognition model of visual perception is experimentally verified on the dataset, and 72.7% of mAPs are obtained. The inference speed reaches 66.7 frames per second on the NVIDIA 1080Ti GPU.

Key words: target recognition, lightweight, pruning