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The traffic density estimation algorithm usually runs on an embedded platform, and its efficiency is very important to the implementation of applications. However, most of the existing methods rely on heavy backbone networks, require high-performance hardware platform support and take a long time to calculate, which are difficult prerequisites to apply in practice. In consideration of the aforementioned problems, this paper proposes a lightweight traffic density estimation method based on structured knowledge transfer (Le-SKT). This method consists of a teacher network and student network. In the teacher network, in order to solve the problem of variable vehicle scales in video images, a multiscale fusion structure is designed, which can fully fuse the low-level and high-level features of an image. To solve the problem of background interference in video images, a fusion module based on an attention mechanism is constructed to enhance the useful traffic flow feature information and suppress the background information. At the same time, the fused traffic flow feature information is dilated convoluted to expand the receptive field and obtain richer contextual vehicle feature information, which generates a trained teacher network. Finally, a lightweight student network is generated by using the structured knowledge of the teacher network, which solves the problems of long computing times and high hardware requirements. The MAE and GAME of this method are reduced to 3.92 and 5.69, respectively in the TRANCOS dataset and 3.74 and 5.57, respectively in the VisDrone2019 vehicle dataset, respectively; The parameter quantity of the student network is only 0.65% of that of the teacher network, which greatly reduces the scale of the neural network model and is suitable for an embedded computing platform with low performance. [ABSTRACT FROM AUTHOR]