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篇名 |
A Dual-branch CNN Structure for Deformable Object Detection
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並列篇名 | A Dual-branch CNN Structure for Deformable Object Detection |
作者 | Jianjun Li、Kai Zheng、Zhenxing Luo、Zhuo Tang、Ching-Chun Chang |
英文摘要 | Object detectors based on CNN are now able to achieve satisfactory accuracy, but their ability to deal with some targets with geometric deformation or occlusion is often poor. This is largely due to the fixed geometric structure of the convolution kernel and the single inflexible network structure. In our work, we use dual branch parallel processing to extract the different features of the target area to coordinate the prediction. To further enhance the performance of the network, this study rebuilds the feature extraction module. Finally, our detector learns to adapt to a variety of different shapes and sizes. The proposed method achieves up to 81.76% mAP on the Pascal VOC2007 dataset and 79.6% mAP on the Pascal VOC2012 dataset. |
起訖頁 | 811-818 |
關鍵詞 | Dual-branch structure、Convolution neural network、Deformable object detection |
刊名 | 網際網路技術學刊 |
期數 | 202005 (21:3期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
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