1 research outputs found
Feature Fusion Detector for Semantic Cognition of Remote Sensing
The value of remote sensing images is of vital importance in many areas and
needs to be refined by some cognitive approaches. The remote sensing detection
is an appropriate way to achieve the semantic cognition. However, such
detection is a challenging issue for scale diversity, diversity of views, small
objects, sophisticated light and shadow backgrounds. In this article, inspired
by the state-of-the-art detection framework FPN, we propose a novel approach
for constructing a feature fusion module that optimizes feature context
utilization in detection, calling our system LFFN for Layer-weakening Feature
Fusion Network. We explore the inherent relevance of different layers to the
final decision, and the incentives of higher-level features to lower-level
features. More importantly, we explore the characteristics of different
backbone networks in the mining of basic features and the correlation
utilization of convolutional channels, and call our upgraded version as
advanced LFFN. Based on experiments on the remote sensing dataset from Google
Earth, our LFFN has proved effective and practical for the semantic cognition
of remote sensing, achieving 89% mAP which is 4.1% higher than that of FPN.
Moreover, in terms of the generalization performance, LFFN achieves 79.9% mAP
on VOC 2007 and achieves 73.0% mAP on VOC 2012 test, and advacned LFFN obtains
the mAP values of 80.7% and 74.4% on VOC 2007 and 2012 respectively,
outperforming the comparable state-of-the-art SSD and Faster R-CNN models.Comment: 12 pages,6 figure