Research Article
Earf-YOLO: An Efficient Attention Receptive Field Model for Recognizing Symbols of Zhuang Minority Patterns
Table 1
Comparison of AP of Earf-YOLO with that of other latest models on the datasets of Zhuang pattern symbols.
| Methods | The backbone network | AP(%) | AP50(%) | AP75(%) | APS(%) | APM(%) | APL(%) |
| YOLOv4 640(baseline) [11] | ResNet-101 | 36.8 | 56.2 | 39.2 | 20.4 | 39.8 | 46.4 | YOLOv5s8 | ResNet-101 | 37.7 | 56.8 | 40.4 | 21.1 | 40.8 | 47.6 | Libra RetinaNet [39] | ResNet-101 | 36.5 | 55.7 | 39.1 | 21.0 | 40.6 | 46.3 | RetinaNet w/AugFPN [40] | ResNet-101 | 37.2 | 55.4 | 40.2 | 20.2 | 40.3 | 47.1 | Our model | ResNet-101 | 39.1 | 58.5 | 41.7 | 22.3 | 42.3 | 48.9 |
| RetinaNet w/SABL [41] | CSPDarkNet53 | 36.3 | 59.2 | 39.1 | 18.3 | 39.1 | 48.3 | LRF [42] | CSPDarkNet53 | 38.3 | 60.4 | 41.8 | 20.2 | 41.2 | 50.3 | Faster RCNN [43] | CSPDarkNet53 | 37.4 | 58.6 | 39.8 | 19.8 | 41.9 | 50.2 | YOLOv4 640 [11] | CSPDarkNet53 | 38.2 | 58.6 | 40.9 | 21.3 | 42.6 | 48.3 | RDSNet [44] | CSPDarkNet53 | 40.6 | 59.4 | 41.9 | 21.1 | 41.7 | 50.4 | YOLOX [45] | CSPDarkNet53 | 40.7 | 59.9 | 42.5 | 22.5 | 43.2 | 51.6 | Our model | CSPDarkNet53 | 41.0 | 61.7 | 43.8 | 24.3 | 44.4 | 51.8 |
|
|