Abstract
Object detection methods can be divided into two categories that are the two-stage methods with higher accuracy but lower speed and the one-stage methods with lower accuracy but higher speed. In order to inherit the advantages of both approaches, a novel dense object detector, called Path Augmented RetinaNet (PA-RetinaNet), is proposed in this paper. It not only achieves a better accuracy than the two-stage methods, but also maintains the efficiency of the one-stage methods. Specifically, we introduce a bottom-up path augmentation module to enhance the feature exaction hierarchy, which shortens the information path between lower feature layers and topmost layers. Furthermore, we address the class imbalance problem by introducing a Class-Imbalance loss, where the loss of each training sample is weighted by a function of its predicted probability, so that the trained model focuses more on hard examples. To evaluate the effectiveness of our PA-RetinaNet, we conducted a number of experiments on the MS COCO dataset. The results show that our method is 4.3% higher than the existing two-stage method, while the speed is similar to the state-of-the-art one-stage methods.
Supported by the National Key R&D Program of China (2018YFB0203904), National Natural Science Foundation of China (61602165) and Natural Science Foundation of Hunan Province (2018JJ3074), NSFC from PRC (61872137, 61502158), Hunan NSF (2017JJ3042).
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Notes
- 1.
Lin et al. [9] found \(\gamma \) = 2 to work best through a large number of experiments. The function in this paper is mainly compared with the focal loss at \(\gamma \) = 2.
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Acknowledgments
This project is an improvement on yhenon’s work, thanks for the code provided by yhenon (https://github.com/yhenon/pytorch-retinanet).
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Tan, G., Guo, Z., Xiao, Y. (2019). PA-RetinaNet: Path Augmented RetinaNet for Dense Object Detection. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning. ICANN 2019. Lecture Notes in Computer Science(), vol 11728. Springer, Cham. https://doi.org/10.1007/978-3-030-30484-3_12
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