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A lightweight network for vehicle detection based on embedded system

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Abstract

The target detection algorithm has a high accuracy rate for the detection of general objects. However, for specific kinds of objects, especially those with complex application scenarios and high requirements on recognition rates, the accuracy and real-time performance are still far behind the actual application requirements. Meanwhile, the high real-time performance of most current target detection algorithm depends on high-performance computers or Graphics Processing Units (GPUs), which limits their application in low-performance embedded system. In this paper, an embedded vehicle detection algorithm was proposed. Compared to the existing general detection algorithms, the network structure of the proposed algorithm has been improved in many aspects. Firstly, the overall network structure of YOLOv3-tiny has been improved, and the way of feature extraction has been modified to increase the network in terms of detection speed and recognition accuracy. Secondly, a spatial pyramid pooling structure is introduced into the network to enhance the learning capability, and the spatial pyramid pooling structure is also improved by increasing the number of pooling layers in the structure and cascading multiple groups of pooling layers of different sizes. Thirdly, to speed up target detection process, the original clustering algorithm is replaced by Kmean++ clustering algorithm, and the original IoU (The Intersection over Union) loss function is replaced by GIoU (Generalized IoU). Fourthly, according to the scale factor of batch normalization layer, the model is compressed and trimmed to remove redundant channels, and a small model more suitable for embedded system application is obtained. Finally, Raspberry Pi 4B and Inter NCS2 neural computing stick are used as the embedded system with the input image size of 416 × 416. Experimental tests on UA-DETRAC dataset obtain higher mAP than other advanced existing methods, indicating that the network model proposed in this paper could effectively improve the real time and accuracy of multi-vehicle target detection.

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Funding

Science and Technology Cooperation Project of The Xinjiang Production and Construction Corps (No. 2019BC008).

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Authors and Affiliations

  1. College of Information Engineering, Tarim University, Alar, 843300, China

    Huanhuan Wu & Yuantao Hua

  2. School of Computer Science, Wuhan University, Wuhan, 430071, China

    Hua Zou

  3. Department of Computer Engineering, Dongguan Polytechnic, Dongguan, 523808, China

    Gang Ke

Authors
  1. Huanhuan Wu
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  2. Yuantao Hua
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  3. Hua Zou
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  4. Gang Ke
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Contributions

Huanhuan Wu contributed to conceptualization, methodology, validation, investigation, writing. Yuantao Hua contributed to methodology, validation, model compressed. Hua Zou contributed to conceptualization, methodology, formal analysis, visualization, funding acquisition, validation, writing—review, editing, supervision. Gang Ke contributed to data curation, validation.

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Correspondence to Hua Zou.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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No need ethical approval Science and Technology Project of Tarim University (No. TDZKZD202104).

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Wu, H., Hua, Y., Zou, H. et al. A lightweight network for vehicle detection based on embedded system. J Supercomput 78, 18209–18224 (2022). https://doi.org/10.1007/s11227-022-04596-z

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  • DOI: https://doi.org/10.1007/s11227-022-04596-z

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