Abstract
Although training object detectors with ImageNet pre-trained models is very common, the models designed for classification are not suitable enough for detection tasks. So, designing a special backbone network for detection tasks is one of the best solutions. In this paper, a backbone network named Dense Receptive Field Network (DRFNet) is proposed for object detection. DRFNet is based on Darknet-60 (our modified version of Darknet-53) and contains a novel architecture named Dense Receptive Field Block (DenseRFB) module. DenseRFB is a densely connected mode of RFB and can form much denser effective receptive fields, which can greatly improve the feature presentation of DRFNet and keep its fast speed. The proposed DRFNet is firstly tested with ScratchDet for fast evaluation. Moreover, as a pre-trained model on ImageNet, DRFNet is also tested with SSD. All the experiments show that DRFNet is an effective and efficient backbone network for object detection.
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Acknowledgements
This work was being supported by the National Natural Science Foundation of China (Grant No. 61402410), the Zhejiang Provincial Science and Technology Planning Key Project of China (Grant No. 2018C01064), and the Zhejiang Provincial Natural Science Foundation of China (Grant No. LY19F020027, LY18F020029).
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Gao, F., Yang, C., Ge, Y., Lu, S., Shao, Q. (2019). Dense Receptive Field Network: A Backbone Network for Object Detection. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing. ICANN 2019. Lecture Notes in Computer Science(), vol 11729. Springer, Cham. https://doi.org/10.1007/978-3-030-30508-6_9
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