A Lightweight Object Detection Method in Aerial Images Based on Dense Feature Fusion Path Aggregation Network

In recent years, significant progress has been obtained in object detection using Convolutional Neural Networks (CNNs). However, owing to the particularity of Remote Sensing Images (RSIs), common object detection methods are not well suited for RSIs. Aiming at the difficulties in RSIs, this paper pr...

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Bibliographic Details
Published inISPRS international journal of geo-information Vol. 11; no. 3; p. 189
Main Authors Zhou, Liming, Rao, Xiaohan, Li, Yahui, Zuo, Xianyu, Qiao, Baojun, Lin, Yinghao
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.03.2022
Subjects
Accuracy
Agglomeration
Aggregation
Algorithms
Artificial neural networks
Backbone
Boxes
Datasets
Deep learning
dense feature fusion
Detection
Detectors
Efficiency
Experiments
Feature extraction
Feature maps
feature reuse module
Information processing
Lightweight
Modules
Neural networks
Object recognition
Remote sensing
residual dense block
Semantics
Sensors
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Summary:In recent years, significant progress has been obtained in object detection using Convolutional Neural Networks (CNNs). However, owing to the particularity of Remote Sensing Images (RSIs), common object detection methods are not well suited for RSIs. Aiming at the difficulties in RSIs, this paper proposes an object detection method based on the Dense Feature Fusion Path Aggregation Network (DFF-PANet). Firstly, for better improving the detection performance of small and medium-sized instances, we propose Feature Reuse Module (FRM), which can integrate semantic and location information contained in feature maps; this module can reuse feature maps in the backbone to enhance the detection capability of small and medium-sized instances. After that, we design the DFF-PANet, which can help feature information extracted from the backbone to be fused more efficiently, and thus cope with the problem of external interference factors. We performed experiments on the Dataset of Object deTection in Aerial images (DOTA) dataset and the HRSC2016 dataset; the accuracy reached 71.5% mAP, which exceeds most object detectors of one-stage and two-stages at present. Meanwhile, the size of our model is only 9.2 M, which satisfies the requirement of being lightweight. The experimental results demonstrate that our method not only has better detection accuracy but also maintains high efficiency in RSIs.
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi11030189