Abstract
Real-time object detection is of great significance to embedded mobile platforms. We propose a lightweight object detection network for embedded devices, which we call ShuffleSSD. The ShuffleNet V2 network is used as the backbone, which can effectively reduce the size of the model. In the proposed lightweight detection model, a single shot multi-box detector is adopted and a receptive field block is integrated to obtain high-quality detection results. The evaluation is performed on the public object detection data set (PASCAL VOC) and compared to the most advanced real-time object detection network. The experimental results show that the proposed network has higher detection accuracy than MobileNet-SSD, with smaller network parameters. It is more suitable for real-time object detection on embedded devices.
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Zhang, Y., Wang, J., Miao, Z., Li, Y., Wang, J. (2019). Shuffle Single Shot Detector. In: Huang, DS., Huang, ZK., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2019. Lecture Notes in Computer Science(), vol 11645. Springer, Cham. https://doi.org/10.1007/978-3-030-26766-7_62
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DOI: https://doi.org/10.1007/978-3-030-26766-7_62
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