Study on automatic target identification method in substation 3D scene model
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Sep 25, 2025
About this article
Published Online: Sep 25, 2025
Received: Feb 01, 2025
Accepted: May 09, 2025
DOI: https://doi.org/10.2478/amns-2025-1009
Keywords
© 2025 Youhui Chen et al., published by Sciendo.
This work is licensed under the Creative Commons Attribution 4.0 International License.
Figure 1.

Figure 2.

Figure 3.

Performance comparison of different algorithms
| Model | mAP@0.5/% | mAP@0.5:0.95/% | Vol/MB | FPS/(f/s) |
|---|---|---|---|---|
| YOLOv5n | 84.62 | 59.08 | 3.88 | 139 |
| YOLOv5s | 87.85 | 63.92 | 14.45 | 125 |
| YOLOv5m | 88.89 | 66.63 | 42.54 | 95 |
| YOLOv5l | 89.15 | 67.25 | 93.81 | 80 |
| YOLOv5x | 89.64 | 67.41 | 175.29 | 55 |
| YOLOv3 | 81.82 | 46.34 | 246.64 | 43 |
| YOLOv4 | 82.06 | 47.49 | 256.41 | 35 |
| YOLOXs | 81.38 | 50.88 | 36.64 | 32 |
| SSD | 40.54 | 20.25 | 98.58 | 43 |
| Faster RCNN | 50.29 | 23.21 | 115.37 | 15 |
| Ours | 90.37 | 68.94 | 8.72 | 92 |
Comparison of the results of the drop sampling experiment
| Method | The number of original clouds | The number of clouds in the back point | Time(s) |
|---|---|---|---|
| Random drop sampling | 281421 | 47251 | 0.1274 |
| Uniform drop sampling | 281421 | 47251 | 0.1532 |
| Body drop sampling | 281421 | 47251 | 0.1049 |
RMSE and time-consuming comparison results of different algorithms
| No. | ICP | RANSAC+ICP | Ours | |||
|---|---|---|---|---|---|---|
| 1 | RMSE | Time/s | RMSE | Time/s | RMSE | Time/s |
| 2 | 0.632 | 0.159 | 0.385 | 0.253 | 0.079 | 0.275 |
| 3 | 0.683 | 0.214 | 0.465 | 0.276 | 0.075 | 0.318 |
| 4 | 0.652 | 0.208 | 0.472 | 0.295 | 0.077 | 0.299 |
| 5 | 0.637 | 0.201 | 0.516 | 0.282 | 0.076 | 0.273 |
| 6 | 0.601 | 0.311 | 0.243 | 0.674 | 0.019 | 0.615 |
| 7 | 0.641 | 0.374 | 0.357 | 0.645 | 0.008 | 0.649 |
| 8 | 0.648 | 0.459 | 0.551 | 1.142 | 0.009 | 1.106 |
| 9 | 0.639 | 0.428 | 0.538 | 0.938 | 0.013 | 0.627 |
| 10 | 0.655 | 0.417 | 0.492 | 0.729 | 0.007 | 1.235 |
Improve the lateral contrast experiment of the module
| Model | mAP@0.5/% | mAP@0.5:0.95/% | Model | mAP@0.5/% | mAP@0.5:0.95/% |
|---|---|---|---|---|---|
| YOLOv5 | 49.84 | 30.49 | FPN | 49.84 | 30.49 |
| SE | 50.69 | 32.95 | PANet | 51.06 | 32.95 |
| BAM | 50.93 | 33.41 | GFPN | 51.98 | 33.16 |
| CBAM | 51.42 | 33.87 | PRFPN | 52.31 | 33.83 |
| CAM | BIFPN | ||||
| Model | Loss value | mAP@0.5/% | Model | Loss value | mAP@0.5/% |
| IoU | 0.0712 | 60.52 | EIoU | 0.0691 | 64.27 |
| GIoU | 0.0694 | 63.45 | SIoU | 0.0685 | 64.41 |
| DIoU | 0.0695 | 62.93 | CIoU |
Removal experiment from the group point
| Method | Parameter | Drop sampling | Removal from the group point | Time(s) |
|---|---|---|---|---|
| Radius filtering | 47251 | 47179 | 0.1042 | |
| 47251 | 47103 | 0.1225 | ||
| 47251 | 47192 | 0.1563 | ||
| Statistical filtering | 47251 | 46935 | 0.0991 | |
| 47251 | 46326 | 0.1208 | ||
| 47251 | 45834 | 0.1517 |
The experimental results of the model
| Group | BiFPN | CAM | CIoU | Vol/MB | FPS/(f/s) | mAP@0.5/% | mAP@0.5:0.95/% |
|---|---|---|---|---|---|---|---|
| 1 | × | × | × | 14.45 | 125 | 87.85 | 63.92 |
| 2 | √ | × | × | 13.98 | 103 | 87.94 | 64.06 |
| 3 | × | √ | × | 12.07 | 101 | 88.06 | 64.73 |
| 4 | × | × | √ | 14.45 | 125 | 87.85 | 63.92 |
| 5 | √ | √ | × | 11.39 | 100 | 88.74 | 65.97 |
| 6 | √ | × | √ | 13.98 | 98 | 89.23 | 66.48 |
| 7 | × | √ | √ | 10.86 | 95 | 89.62 | 67.81 |
| 8 | √ | √ | √ | 8.72 | 92 | 90.37 | 68.94 |
