Feature enhanced faster R-CNN for object detection

Jun Jiang; Zhongbing Hu

doi:10.1117/12.2539211

14 February 2020 Feature enhanced faster R-CNN for object detection

Jun Jiang, Zhongbing Hu

Proceedings Volume 11429, MIPPR 2019: Automatic Target Recognition and Navigation; 114290S (2020) https://doi.org/10.1117/12.2539211
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

In recent years, deep convolutional neural networks (CNNs) have achieved great successes in object detection, however, feature extraction is still sensitive to scale variation. FPN is one of the majority strategies to deal with this problem. It uses a top-down pathway and lateral connection to combine high-level features with low-level features, and then generates robust features. However, in FPN, high-level features are still unable to capture the detail information, and this results in the inconsistent representations for the same objects with different scales. To solve this problem, we proposed a Feature Enhanced Module to get more robust features, which can help the networks to produce object localization with higher quality, i.e., without bells and whistles. The performance of the proposed method is shown by the experiments in which it achieves a 1.1 point AP50 gain and 2.3 point AP75 gain on the Pascal VOC dataset, comparing to the Faster RCNN with FPN.

Citation Download Citation

Jun Jiang and Zhongbing Hu "Feature enhanced faster R-CNN for object detection", Proc. SPIE 11429, MIPPR 2019: Automatic Target Recognition and Navigation, 114290S (14 February 2020); https://doi.org/10.1117/12.2539211

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KEYWORDS

Sensors

Finite element methods

Computer vision technology

Machine vision

Sun

Convolution

Lithium

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