DC-YOLOv3: A novel efficient object detection algorithm
Feature pyramids have become an essential component in most modern object detectors, such as Mask RCNN, YOLOv3, RetinaNet. In these detectors, the pyramidal feature representations are commonly used which represent an image with multi-scale feature layers. However, the detectors can’t be used in man...
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| Veröffentlicht in: | Journal of physics. Conference series Jg. 2082; H. 1; S. 12012 - 12017 |
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| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
IOP Publishing
01.11.2021
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| ISSN: | 1742-6588, 1742-6596 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | Feature pyramids have become an essential component in most modern object detectors, such as Mask RCNN, YOLOv3, RetinaNet. In these detectors, the pyramidal feature representations are commonly used which represent an image with multi-scale feature layers. However, the detectors can’t be used in many real world applications which require real time performance under a computationally limited circumstance. In the paper, we study network architecture in YOLOv3 and modify the classical backbone--darknet53 of YOLOv3 by using a group of convolutions and dilated convolutions (DC). Then, a novel one-stage object detection network framework called DC-YOLOv3 is proposed. A lot of experiments on the Pascal 2017 benchmark prove the effectiveness of our framework. The results illustrate that DC-YOLOv3 achieves comparable results with YOLOv3 while being about 1.32× faster in training time and 1.38× faster in inference time. |
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| ISSN: | 1742-6588 1742-6596 |
| DOI: | 10.1088/1742-6596/2082/1/012012 |