Paper Info
Title
Military Vehicle Object Detection Based on Hierarchical Feature Representation and Refined Localization
Abstract
Military vehicle object detection technology in complex environments is the basis for the implementation of reconnaissance and tracking tasks for weapons and equipment, and is of great significance for information and intelligent combat. In response to the poor performance of traditional detection algorithms in military vehicle detection, we propose a military vehicle detection method based on hierarchical feature representation and reinforcement learning refinement localization, referred to as MVODM. First, for the military vehicle detection task, we construct a reliable dataset MVD. Second, we design two strategies, hierarchical feature representation and reinforcement learning-based refinement localization, to improve the detector. The hierarchical feature representation strategy can help the detector select the feature representation layer suitable for the object scale, and the reinforcement learning-based refinement localization strategy can improve the accuracy of the object localization boxes. The combination of these two strategies can effectively improve the performance of the detector. Finally, the experimental results on the homemade dataset show that our proposed MVODM has excellent detection performance and can better accomplish the detection task of military vehicles.
Year
DOI
Venue
2022
10.1109/ACCESS.2022.3207153
IEEE ACCESS
Keywords
DocType
Volume
Military vehicles, Object detection, Feature extraction, Task analysis, Detectors, Location awareness, Reinforcement learning, Military vehicle objects, object detection, reinforcement learning, hierarchical feature representation
Journal
10
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Yan Ouyang100.68
Xinqing Wang200.68
Ruizhe Hu300.68
Honghui Xu401.01
Faming Shao502.70