A Mixed Two-stage Object Detector for Image Processing of Power System Applications
| Autor: | Long Xitian, Liu Rui, Chi Yingying, Zhe Zheng |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry Deep learning 05 social sciences Detector Feature extraction Image processing 010501 environmental sciences 01 natural sciences Object detection Electric power system Position (vector) 0502 economics and business Computer vision Stage (hydrology) Artificial intelligence 050207 economics business 0105 earth and related environmental sciences |
| Zdroj: | ICCT |
| DOI: | 10.1109/icct50939.2020.9295843 |
| Popis: | Object detection algorithms based on deep learning has become increasingly important in Image Processing. This paper proposes a deep learning method for improving the objects detection accuracy while supporting a real-time operation by optimizing SSD model into a mixed Two-stage detection model with an extra dense small object detection module which can localize the precise position and give the classification of the dense small objects, using a fully convolutional network. Compared with the SOTA detection model such as two-stage Faster R-CNN and single-stage SSD, our approach has a better trade-off between accuracy and efficiency. The experimental results outperform Faster R-CNN in detection speed and considerably better than SSD in detection accuracy. |
| Databáze: | OpenAIRE |
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