[1]GONG Dahan,YU Longlong,CHEN Hui,et al.Object detection model optimization method for car-level chips[J].CAAI Transactions on Intelligent Systems,2021,16(5):900-907.[doi:10.11992/tis.202107057]
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Object detection model optimization method for car-level chips

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 5 Page number: 900-907 Column: 吴文俊人工智能科技进步奖一等奖 Public date: 2021-09-05
Title:
Object detection model optimization method for car-level chips
Author(s):
GONG Dahan1; 2;  YU Longlong3;  CHEN Hui2; 4;  YANG Fan1; 2;  LUO Pei5;  DING Guiguang1; 2
1. School of Software, Tsinghua University, Beijing 100084, China;
2. BNRist Tsinghua University, Beijing 100084, China;
3. Zhuoxi Institute of Brain and Intelligence, Hangzhou 311121, China;
4. Department of Automation, Tsinghua University, Beijing 100084, China;
5. HoloMatic Technology (Beijing) Co., Ltd, Beijing 100102, China
Keywords:
artificial intelligence; computer vision; object detection; terminal equipment; car-level chip; convolutional neural network; model acceleration; model quantization
CLC:
TP18
DOI:
10.11992/tis.202107057
Abstract:
Convolutional neural networks have achieved great success in visual perception tasks. Its complex network structure makes the model computationally complex, which limits its application in actual terminal scenarios such as autonomous driving. Aiming at the problem of limited computing resources in terminal scenarios, in this paper, we conduct research from two aspects: lightweight deep model design and the model deployment and verification on car-level chips. As for the contradiction between the calculation efficiency of deep models and the detection accuracy, we design a lightweight object detection model based on the center-convolution, enjoying low power consumption and high accuracy model performance. Furthermore, based on the method of quantization aware training, we carried out system-level deployment and verification on car-level chips. We successfully implemented a high-efficiency object detection model on the car-level chips, i.e. tda4, and achieved good performance in autonomous driving scenarios.
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