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http://hdl.handle.net/10397/96590
Title: | DWANet : focus on foreground features for more accurate location | Authors: | Hu, J Zheng, Y Lam, KM Lou, P |
Issue Date: | 2022 | Source: | IEEE access, 2022, v. 10, p. 30716-30729 | Abstract: | Object detection can locate objects in an image using bounding boxes, which can facilitate classification and image understanding, resulting in a wide range of applications. Knowing how to mine useful features from images and detect objects of different scales have become the focus for object-detection research. In this paper, considering the importance of foreground features in the process of object detection, a foreground feature extraction module, based on deformable convolution, is proposed, and the attention mechanism is integrated to suppress the interference from the background. To learn effective features, considering that different layers in a convolutional neural network have different contributions, we propose methods to learn the weights for feature fusion. Experiments on the VOC datasets and COCO datasets show that the proposed algorithm can effectively improve the object detection accuracy, which is 12.1% higher than Faster R-CNN, 1.5% higher than RefineDet, and 2.3% higher than the Hierarchical Shot Detector (HSD). | Keywords: | Feature fusion Foreground features Multi-scale Object detection |
Publisher: | Institute of Electrical and Electronics Engineers | Journal: | IEEE access | ISSN: | 2169-3536 | DOI: | 10.1109/ACCESS.2022.3158681 | Rights: | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ The following publication Hu, J., Zheng, Y., Lam, K. M., & Lou, P. (2022). DWANet: Focus on Foreground Features for More Accurate Location. IEEE Access, 10, 30716-30729 is available at https://doi.org/10.1109/ACCESS.2022.3158681. |
Appears in Collections: | Journal/Magazine Article |
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Hu_DWANet_Focus_Foreground.pdf | 6.16 MB | Adobe PDF | View/Open |
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