Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (18): 131-136.

### Lightweight Target Recognition Deep Neural Network and Its Application

FU Zuoyi, ZHOU Shijie, LI Dinggen

1. 1.School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
2.China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan 430074, China
• Online:2020-09-15 Published:2020-09-10

### 轻量级目标识别深度神经网络及其应用

1. 1.华中科技大学 能源与动力工程学院，武汉 430074
2.华中科技大学 中欧清洁与可再生能源学院，武汉 430074

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