Type recognition of highway trucks based on deep learning

ZHANG Nian; ZHANG Liang

doi:10.19818/j.cnki.1671-1637.2023.01.020

Volume 23 Issue 1

Feb. 2023

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ZHANG Nian, ZHANG Liang. Type recognition of highway trucks based on deep learning[J]. Journal of Traffic and Transportation Engineering, 2023, 23(1): 267-279. doi: 10.19818/j.cnki.1671-1637.2023.01.020

Citation:

ZHANG Nian, ZHANG Liang. Type recognition of highway trucks based on deep learning[J]. Journal of Traffic and Transportation Engineering, 2023, 23(1): 267-279. doi: 10.19818/j.cnki.1671-1637.2023.01.020

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Type recognition of highway trucks based on deep learning

doi: 10.19818/j.cnki.1671-1637.2023.01.020

ZHANG Nian^{1, 2
,},
ZHANG Liang²

1.
College of Civil Engineering, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China
2.
School of Transportation and Logistics, Taiyuan University of Science and Technology, Taiyuan 030024, Shanxi, China

Funds:

National Natural Science Foundation of China 52178341

Research Project of Shanxi Scholarship Council 2020038

More Information

Author Bio:
ZHANG Nian(1984-), male, associate professor, PhD, zhangnian@tyut.edu.cn
Received Date: 2022-07-31
Available Online: 2023-03-08
Publish Date: 2023-02-25

Abstract

Abstract

A deep learning-based fine target detection method for highway trucks and their wheel axles was proposed to determine the vehicle types of highway trucks and improve the recognization speed and accuracy of vehicle types of trucks. 16 403 side images of trucks were obtained by the road monitoring and network crawling to build a dataset of side images of trucks. The Retinex theory and visual enhancement methods, such as the contrast limited adaptive histogram equalization (CLAHE), were used to preprocess the uneven light images and night vision ones in the collected images. Theoretical analysis and comparative experiments were conducted. The one-stage detection network YOLOv3 was selected as the target detection network for the vehicle type recognition of highway trucks. Then, the detection model was optimized from three aspects, such as adjusting the sizes of prior box and model input and introducing the attention mechanism. For the case that multiple trucks might appear at the same time in a single image, an algorithm based on the mining of target location information was employed to analyze the truck and wheel axle location information. A method was proposed to determine the subordinate relationships between the highway truck and the wheel axle according to the location information of axle center points and truck prediction boxes. Research results show that the vehicle feature information can be enhanced significantly by the image preprocessing. The network performance of the detection model improves after the optimization. The issue of determining the vehicle types of trucks can be well solved by mining and leveraging the target location information. A real-time detection speed of the optimized detection model reaches 47 frames per second. The comprehensive accuracy in recognizing the vehicle types of highway trucks is 94.4%. The method realizes the non-contact, fast, and accurate recognition of vehicle types of highway trucks, provides a new means for the vehicle type recognition of highway trucks. Meeting the construction needs of intelligent traffic systems, the proposed method can be applied to further raise the road service level.
- traffic information engineering,
- highway truck,
- deep learning,
- vehicle type recognition,
- attention mechanism,
- image processing

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References(31)

References

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Type recognition of highway trucks based on deep learning

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