Figure/Table detail
UAV auxiliary insulator defect detection mechanism based on partial convolution and dynamic-static fusion
Journal of Cybersecurity,
2025, 3(1):
86-99.
DOI: 10.20172/j.issn.2097-3136.250108
| Methods | Parameters （M） | FLOPs （G） | SFID | SID | |||||||
| Precision （%） | Recall （%） | mAP50 （%） | Inference time （ms） | Precision （%） | Recall （%） | mAP50 （%） | Inference time （ms） | ||||
| EfficientDet[ | 11.9 | 47.0 | 99.2 | 92.0 | 96.7 | 148.7 | 96.9 | 86.7 | 93.8 | 112.2 | |
| SSD[ | 23.7 | 273.6 | 98.4 | 93.9 | 98.7 | 17.0 | 97.1 | 83.5 | 93.1 | 15.0 | |
| CenterNet[ | 32.7 | 109.7 | 98.6 | 96.8 | 98.3 | 27.5 | 97.4 | 83.0 | 88.5 | 19.2 | |
| Faster R-CNN[ | 136.7 | 401.7 | 60.0 | 94.9 | 86.3 | 47.4 | 60.7 | 77.7 | 75.4 | 39.7 | |
| YOLOv3-tiny[ | 12.1 | 18.9 | 96.1 | 96.8 | 98.1 | 2.1 | 97.2 | 79.4 | 89.5 | 2.9 | |
| YOLOv6-s[ | 16.3 | 44.0 | 98.9 | 97.8 | 99.1 | 3.7 | 91.5 | 84.8 | 89.7 | 6.3 | |
| YOLOv8-m[ | 25.8 | 78.7 | 99.3 | 99.1 | 99.4 | 5.1 | 96.4 | 86.0 | 93.6 | 7.2 | |
| YOLOv9-m[ | 32.5 | 130.7 | 98.2 | 98.5 | 99.3 | 11.2 | 96.2 | 88.2 | 94.0 | 24.4 | |
| YOLOv10-m[ | 16.5 | 63.4 | 97.2 | 97.4 | 99.2 | 6.4 | 96.8 | 83.3 | 90.7 | 7.6 | |
| UID-DETR | 14.1 | 41.4 | 99.4 | 99.2 | 99.4 | 5.7 | 98.3 | 91.8 | 95.4 | 13.5 | |
Table 1 Performance comparison of object detection models on SFID and SID datasets
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