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.
CITATION STYLE
Tan, G., Guo, Z., & Xiao, Y. (2019). PA-RetinaNet: Path Augmented RetinaNet for Dense Object Detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11728 LNCS, pp. 138–149). Springer Verlag. https://doi.org/10.1007/978-3-030-30484-3_12
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