Abstract
Recent studies have achieved great progress on the object detection in terms of accuracy and speed using convolutional neural networks (CNNs). However, no matter the one-stage detector or the two-stage detector, usually it is still a challenging task for them to detect small objects because of the low resolution and fuzzy feature representation. When the training set only contains small objects, the performance degrades drastically. To improve the performance of small object detection, we develop a new two-stage detector similar to Faster-RCNN. At region proposal stage, we adopt the feature pyramid architecture with lateral connections, which makes the semantic feature of small objects more sensitive. Meanwhile, we design specialized anchors to detect the small objects from large resolution image and train the network with focal loss. At classification stage, dense convolutional network is used to strengthen the feature transmission and multiplexing, which leads to more accurate classification with fewer parameters. We experiment with the challenging Tsinghua-Tencent 100K benchmark, and the evaluations demonstrate a significant performance improvement compared with other state-of-art methods.
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Liang, Z., Shao, J., Zhang, D., Gao, L. (2018). Small Object Detection Using Deep Feature Pyramid Networks. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11166. Springer, Cham. https://doi.org/10.1007/978-3-030-00764-5_51
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DOI: https://doi.org/10.1007/978-3-030-00764-5_51
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