Object Detection with Low Capacity GPU Systems Using Improved Faster R-CNN
| Autor: | Atakan Körez, Necaattin Barişçi |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
010504 meteorology & atmospheric sciences
Computer science 0211 other engineering and technologies Graphics processing unit Normalization (image processing) 02 engineering and technology 01 natural sciences Convolutional neural network Field (computer science) Convolution remote sensing General Materials Science Computer vision Instrumentation 021101 geological & geomatics engineering 0105 earth and related environmental sciences Fluid Flow and Transfer Processes deformable convolutional network weight standardization business.industry Process Chemistry and Technology Deep learning General Engineering deep learning object detection Grid Object detection Computer Science Applications Artificial intelligence business |
| Zdroj: | Applied Sciences Volume 10 Issue 1 |
| ISSN: | 2076-3417 |
| Popis: | Object detection in remote sensing images has been frequently used in a wide range of areas such as land planning, city monitoring, traffic monitoring, and agricultural applications. It is essential in the field of aerial and satellite image analysis but it is also a challenge. To overcome this challenging problem, there are many object detection models using convolutional neural networks (CNN). The deformable convolutional structure has been introduced to eliminate the disadvantage of the fixed grid structure of the convolutional neural networks. In this study, a multi-scale Faster R-CNN method based on deformable convolution is proposed for single/low graphics processing unit (GPU) systems. Weight standardization (WS) is used instead of batch normalization (BN) to make the proposed model more efficient for a small batch size (1 img/per GPU) on single GPU systems. Experiments were conducted on the publicly available 10-class geospatial object detection (NWPU-VHR 10) dataset to evaluate the object detection performance of the proposed model. Experiment results show that our model achieved a 92.3 mAP. This is a 1.7% mAP increase when compared to the best results in the models using the same dataset. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|