A Novel Effective Vehicle Detection Method Based on Swin Transformer in Hazy Scenes

Zaiming Sun, Chang’an Liu, Hongquan Qu and Guangda Xie
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Zaiming Sun: School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Chang’an Liu: Information College, North China University of Technology, Beijing 100144, China
Hongquan Qu: Information College, North China University of Technology, Beijing 100144, China
Guangda Xie: School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China

Mathematics, 2022, vol. 10, issue 13, 1-15

Abstract: Under bad weather, the ability of intelligent vehicles to perceive the environment accurately is an important research content in many practical applications such as smart cities and unmanned driving. In order to improve vehicle environment perception technology in real hazy scenes, we propose an effective detection algorithm based on Swin Transformer for hazy vehicle detection. This algorithm includes two aspects. First of all, for the aspect of the difficulty in extracting haze features with poor visibility, a dehazing network is designed to obtain high-quality haze-free output through encoding and decoding methods using Swin Transformer blocks. In addition, for the aspect of the difficulty of vehicle detection in hazy images, a new end-to-end vehicle detection model in hazy days is constructed by fusing the dehazing module and the Swin Transformer detection module. In the training stage, the self-made dataset Haze-Car is used, and the haze detection model parameters are initialized by using the dehazing model and Swin-T through transfer learning. Finally, the final haze detection model is obtained by fine tuning. Through the joint learning of dehazing and object detection and comparative experiments on the self-made real hazy image dataset, it can be seen that the detection performance of the model in real-world scenes is improved by 12.5%.

Keywords: Swin Transformer; image dehazing; vehicle detection; multi-scale feature (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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