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一个轻量级的交叉尺度EDS-DETR模型用于在传输走廊中检测危险
He Su1, Jiaomin Liu1, Zhenzhou Wang2
1Provincial and Ministerial Co-construction Collaborative Innovation Center on Reliability Technology of Electrical Products, Hebei University of Technology, Tianjin, 300401, China.
Scientific reports
|December 15, 2025
概括
一个新的轻量级模型,EDS-DETR,增强了对电力传输走廊的视觉检查. 它提高了对外部危险的检测准确性和效率,确保了更安全的基础设施.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 电气工程 电气工程
背景情况:
- 视觉检查对于识别电力传输通道中的外部危险至关重要.
- 卷积神经网络 (CNN) 在多尺度目标检测和平衡准确性方面面临挑战,其轻量级设计适用于复杂的环境.
研究的目的:
- 提出一个轻量级的跨尺度检测模型,EDS-DETR,以更好地识别传输走廊中的外部危险.
- 提高视觉检查技术的准确性,效率和实时性能.
主要方法:
- 改进了ResNet18骨干,具有高效的多尺度注意力和部分卷积,以提高计算效率和表示.
- 在编码器中引入DySample,以最小的成本恢复特征分辨率,保存细节并增强动态感知.
- 采用形状IoU损失函数来提高检测准确性和加速模型的融合.
主要成果:
- 在定制数据集上,EDS-DETR实现了高性能指标:91.4%的精度,85.1%的回忆和93.1%的mAP@0.5.
- 与基线相比,模型效率显著,参数减少13.4%,模型大小减少14.8%.
- 实现了 190 FPS 的实时推断速度,满足了动力传输安全的实际要求.
结论:
- 通过改进视觉检查,EDS-DETR模型是有效和实用的,可以提高功率传输的可靠性和安全性.
- 拟议的模型成功地解决了复杂环境中的传统CNN的局限性,提供了准确性和效率的平衡.
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