DOUNet: Dynamic Optimization and Update Network for Oriented Object Detection
Source abstract
Abstract
Object detection can accurately identify and locate targets in images, serving basic industries such as agricultural monitoring and urban planning. However, targets in remote sensing images have random rotation angles, which hinders the accuracy of remote sensing image object detection algorithms. In addition, due to the long-tailed distribution of detected objects in remote sensing images, the network finds it difficult to adapt to imbalanced datasets. In this article, we designed and proposed the Dynamic Optimization and Update network (DOUNet). By introducing adaptive rotation convolution to replace 2D convolution in the Region Proposal Network (RPN), the features of rotating targets are effectively extracted. To address the issues caused by imbalanced data, we have designed a long-tail data detection module to collect features of tail categories and guide the network to output more balanced detection results. Various experiments have shown that after two stages of feature learning and classifier learning, our designed network can achieve optimal performance and perform better in detecting imbalanced data.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Vehicle License Plate Recognition · Advanced Neural Network Applications · Retinal Imaging and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, School of Automation, Harbin University of Science and Technology, Harbin 150080, China
School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
Funding
Key R&D Program Guidance Projects of Heilongjiang Province · Natural Science Foundation of Heilongjiang Province
References (25)
Show source-provided references
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