TensorRT acceleration based on deep learning photoelectric target detection

Shicheng Zhang; Laixian Zhang; Mingyu Qin; Huichao Guo

doi:10.1117/12.2655302

7 October 2022 TensorRT acceleration based on deep learning photoelectric target detection

Shicheng Zhang, Laixian Zhang, Mingyu Qin, Huichao Guo

Proceedings Volume 12344, International Conference on Intelligent and Human-Computer Interaction Technology (IHCIT 2022); 1234418 (2022) https://doi.org/10.1117/12.2655302
Event: International Conference on Intelligent and Human-Computer Interaction Technology (IHCIT 2022), 2022, Zhuhai, China

Abstract

One of the important research directions in the field of target detection in computer vision, among which deep learning-based target detection can extract advanced features and has higher detection accuracy than traditional detection algorithms. The inference speed of convolutional neural networks in embedded platforms is low, and the practical application value is low. Therefore, for the background of optoelectronic device detection and camera detection, on the embedded platform NVIDIA Jeston Nano, the ResNet18 convolutional neural network is used to identify the photoelectric target. Use TensorRT to accelerate the process of network model simplification and engine construction, and accelerate the network inference time. Experimental results show that when the input image resolution is 640*480, the inference time of tensorRT technology after running the network on the NVIDIA Jeston Nano device is in the range of 0.04-0.06s, and the single-area photoelectric target detection inference is accelerated by 2.38 times and the multi-area photoelectric target detection inference is accelerated by 2.74 times, which provides support for practical applications.

Citation Download Citation

Shicheng Zhang, Laixian Zhang, Mingyu Qin, and Huichao Guo "TensorRT acceleration based on deep learning photoelectric target detection", Proc. SPIE 12344, International Conference on Intelligent and Human-Computer Interaction Technology (IHCIT 2022), 1234418 (7 October 2022); https://doi.org/10.1117/12.2655302

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available