Abstract
While recent object detection approaches have greatly improved the accuracy and robustness, the detection speed remains a Challenge for the community. In this paper, we propose an efficient fully convolutional network (EFCN) for real time object detection. EFCN employs the lightweight MobileNet [1] as the base network to significantly reduce the computation cost. Meanwhile, it detects objects in feature maps with multiple scales, and deploys a refining module on the top of each of these feature maps to alleviate the accuracy loss brought by the simple base network. We evaluate EFCN on the challenging KITTI [2] dataset and compare it with the state-of-the-art methods. The results show that EFCN keeps a good balance between speed and accuracy, it has \(25{\times }\) fewer parameters and is up to \(31{\times }\) faster than Faster-RCNN [3] while maintaining similar or better accuracy.
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Acknowledgments
This work was partially supported by the National Natural Science Foundations of China (Grant Nos. 61472386 and 61502444).
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Zhou, X., Feng, YJ., Zhou, X. (2017). Real-Time Object Detection Using Efficient Convolutional Networks. In: Zhou, J., et al. Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science(), vol 10568. Springer, Cham. https://doi.org/10.1007/978-3-319-69923-3_68
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DOI: https://doi.org/10.1007/978-3-319-69923-3_68
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