A New Architecture of Feature Pyramid Network for Object Detection
| Autor: | Young Shik Moon, Yichen Zhang, Jeong Hoon Han, Yong Woo Kwon |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
business.industry
Computer science Deep learning Detector Pattern recognition 02 engineering and technology Pascal (programming language) 010501 environmental sciences Object (computer science) 01 natural sciences Object detection Feature (computer vision) Pyramid 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Architecture business computer 0105 earth and related environmental sciences computer.programming_language |
| Zdroj: | 2020 IEEE 6th International Conference on Computer and Communications (ICCC). |
| DOI: | 10.1109/iccc51575.2020.9345302 |
| Popis: | In recent years, object detectors generally use the feature pyramid network (FPN) to solve the problem of scale variation in object detection. In this paper, we propose a new architecture of feature pyramid network which combines a top-down feature pyramid network and a bottom-up feature pyramid network. The main contributions of the proposed method are two-fold: (1) We design a more complex feature pyramid network to get the feature maps for object detection. (2) By combining these two architectures, we can get the feature maps with richer semantic information to solve the problem of scale variation better. The proposed method experiments on PASCAL VOC2007 dataset. Experimental results show that the proposed method can improve the accuracy of detectors using the FPN by about 1.67%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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