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Automatic Driving Scenarios: A Cross-Domain Approach for Object Detection

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Artificial Neural Networks and Machine Learning – ICANN 2023 (ICANN 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14260))

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Abstract

As autonomous driving technology advances, the need for accurate and robust object detection in various driving environments has become more urgent. However, domain adaptation presents a significant challenge due to the impact of weather, lighting, and scene context on object detection models. To address this issue, we propose a new method that utilizes pseudo-labels. Our approach involves two modules: the Category-Adversarial-Adaptive (CAA) and the Regression-Adversarial-Adaptive (RAA), which generate pseudo-labels. The detector is then trained on both source domain data and the target domain with pseudo-labels, resulting in improved cross-domain performance. The CAA and RAA modules operate independently and complement each other, allowing them to adapt to their respective detection tasks without interference. Furthermore, we demonstrate the effectiveness of loss smoothing in enhancing the model’s generalization performance. Our experimental results indicate that our model outperforms classic models, achieving improvements of 0.9\(\%\), 2\(\%\), and 1.4\(\%\) in the cross-domain challenges of SIM10K to Cityscape, KITTI to Cityscape, and Cityscape to foggyCityscape, respectively.

This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 62001103 and U1936201.

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Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Southeast University, Nanjing, 210096, China

    Shengheng Liu & Yongming Huang

  2. Southeast University-Monash University Joint Graduate School, Suzhou, 215123, China

    Shengheng Liu & Jiacheng Chen

  3. Hella Shanghai Electronics Co., Ltd., Shanghai, 201201, China

    Lei Li

  4. China Academy of Electronics and Information Technology, Beijing, 100041, China

    Yahui Ma

Authors
  1. Shengheng Liu
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  2. Jiacheng Chen
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  3. Lei Li
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  4. Yahui Ma
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  5. Yongming Huang
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Correspondence to Shengheng Liu .

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Editors and Affiliations

  1. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

  3. Lancaster University, Lancaster, UK

    Plamen Angelov

  4. Teesside University, Middlesbrough, UK

    Chrisina Jayne

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Liu, S., Chen, J., Li, L., Ma, Y., Huang, Y. (2023). Automatic Driving Scenarios: A Cross-Domain Approach for Object Detection. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14260. Springer, Cham. https://doi.org/10.1007/978-3-031-44195-0_4

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  • DOI: https://doi.org/10.1007/978-3-031-44195-0_4

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  • Online ISBN: 978-3-031-44195-0

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