Vehicle-based Thermal Imaging Target Detection Method Based on Enhanced Lightweight Network

YI Shi; ZHOU Siyao; SHEN Lian; ZHU Jinming

Volume 43 Issue 3

Apr. 2021

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YI Shi, ZHOU Siyao, SHEN Lian, ZHU Jinming. Vehicle-based Thermal Imaging Target Detection Method Based on Enhanced Lightweight Network[J]. Infrared Technology , 2021, 43(3): 237-245.

Citation:

YI Shi, ZHOU Siyao, SHEN Lian, ZHU Jinming. Vehicle-based Thermal Imaging Target Detection Method Based on Enhanced Lightweight Network[J]. Infrared Technology , 2021, 43(3): 237-245.

YI Shi, ZHOU Siyao, SHEN Lian, ZHU Jinming. Vehicle-based Thermal Imaging Target Detection Method Based on Enhanced Lightweight Network[J]. Infrared Technology , 2021, 43(3): 237-245.

Citation:

YI Shi, ZHOU Siyao, SHEN Lian, ZHU Jinming. Vehicle-based Thermal Imaging Target Detection Method Based on Enhanced Lightweight Network[J]. Infrared Technology , 2021, 43(3): 237-245.

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Vehicle-based Thermal Imaging Target Detection Method Based on Enhanced Lightweight Network

College of Information Science and Technology, Chengdu University of Technology, Chengdu 610059, China

Received Date: 2018-09-11
Rev Recd Date: 2018-12-21
Publish Date: 2021-04-02

Abstract

Abstract

A vehicle-based thermal imaging system does not depend on a light source, is insensitive to weather, and has a long detection distance. Automatic target detection using vehicle-based thermal imaging is of great significance for intelligent night driving. Compared with visible images, the infrared images acquired by a vehicle-based thermal imaging system based on existing algorithms have low resolution, and the details of small long-range targets are blurred. Moreover, the real-time algorithm performance required to address the vehicle speed and computing ability of the vehicle-embedded platform should be considered in the vehicle-based thermal imaging target detection method. To solve these problems, an enhanced lightweight infrared target detection network (I-YOLO) for a vehicle-based thermal imaging system is proposed in this study. The network uses a tiny you only look once(Tiny-YOLOV3) infrastructure to extract shallow convolution-layer features according to the characteristics of infrared images to improve the detection of small infrared targets. A single-channel convolutional core was used to reduce the amount of computation. A detection method based on a CenterNet structure is used to reduce the false detection rate and improve the detection speed. The actual test shows that the average detection rate of the I-YOLO target detection network in vehicle-based thermal imaging target detection reached 91%, while the average detection speed was81 fps, and the weight of the training model was96MB, which is suitable for deployment on a vehicle-based embedded system.
- vehicle thermal imaging system,
- night intelligent driving,
- I-YOLO infrared target detection network,
- CenterNet structure,
- vehicle embedded platform

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References(19)

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