Abstract
Current rotating object detection task achieves good results based on large models. In order to reduce the size of model, we propose a lightweight network SFC (ShuffleNet combines FPN with CSL) for rotating target detection. SFC first introduces circular smooth label (CSL) to detect target rotations, which transforms the traditional angle regression problem into classification problem. Then, the lightweight ShuffleNetV2 is utilized as the backbone to reduce the number of parameters. ShuffleNetV2 is used for feature extraction, and CSL is introduced to address the periodic problem of angles. Comparative experiments were carried out on DOTA 1.5 dataset. The experimental results show that the proposed method reduces the parameter by nearly 90% with a slight loss of accuracy, and increases the inferencing speed by 40% at the same time.
Y. Jiao and Q. Zhu—Contributed equally to this work.
This work is partially supported by the National Natural Science Foundation of China (62101552) and by the Key R &D Program of the Chinese Academy of Sciences (ZDRW-XH-2021-3-03).
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Jiao, Y., Zhu, Q., He, H., Zhao, T., Wang, H. (2022). Rotating Target Detection Based on Lightweight Network. In: Khanna, S., Cao, J., Bai, Q., Xu, G. (eds) PRICAI 2022: Trends in Artificial Intelligence. PRICAI 2022. Lecture Notes in Computer Science, vol 13631. Springer, Cham. https://doi.org/10.1007/978-3-031-20868-3_46
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