Object Detection Network Based on Feature Fusion and Attention Mechanism

Ying Zhang, Yimin Chen, Chen Huang and Mingke Gao
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Ying Zhang: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Yimin Chen: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Chen Huang: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Mingke Gao: The 32nd Research Institute, China Electronics Technology Group Corporation, No. 63 Chengliugong Road, Jiading District, Shanghai 200444, China

Future Internet, 2019, vol. 11, issue 1, 1-14

Abstract: In recent years, almost all of the current top-performing object detection networks use CNN (convolutional neural networks) features. State-of-the-art object detection networks depend on CNN features. In this work, we add feature fusion in the object detection network to obtain a better CNN feature, which incorporates well deep, but semantic, and shallow, but high-resolution, CNN features, thus improving the performance of a small object. Also, the attention mechanism was applied to our object detection network, AF R-CNN (attention mechanism and convolution feature fusion based object detection), to enhance the impact of significant features and weaken background interference. Our AF R-CNN is a single end to end network. We choose the pre-trained network, VGG-16, to extract CNN features. Our detection network is trained on the dataset, PASCAL VOC 2007 and 2012. Empirical evaluation of the PASCAL VOC 2007 dataset demonstrates the effectiveness and improvement of our approach. Our AF R-CNN achieves an object detection accuracy of 75.9% on PASCAL VOC 2007, six points higher than Faster R-CNN.

Keywords: CNN; object detection network; attention mechanism; feature fusion (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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