配电线网施工安全设备旋转目标检测算法

针对传统目标检测算法在配电施工作业场景中对施工安全设备识别精度低和效果不准确的问题,本文提出了一种面向配电线网施工安全设备识别的YOLO-Rotating算法。该算法以YOLOv8为基础,采用深度可分离卷积代替部分Conv设计C2f-R模块,减少模型参数量,提升感受野;使用GAM注意力模块增强特征提取能力,提高语义信息并减少干扰;最后增加旋转目标检测模块使检测框与目标轮廓更贴合,提高检测准确度。实验结果表明,在配电安全设备数据集上,YOLO-Rotating算法的平均精度均值（mAP）达到84.6%,比原算法提高了2.5%,精确度提升了2.07%。该算法具有更高的检测精度和更好的实际应用价值,满足边缘计算设备的要求,适用于配电现网作业施工场景。

Rotating Target Detection Algorithm for Safety Equipment of Distribution Network Construction

Aiming at the problem that the traditional object detection algorithm has low recognition accuracy and inaccurate effect of construction safety equipment in power distribution construction operation scenarios, this paper proposes a YOLO-Rotating algorithm for the identification of construction safety equipment in distribution network. Based on YOLOv8, the algorithm uses deep separable convolution instead of part of Conv to design C2f-R modules, which reduces the number of model parameters and improves the receptive field. Use the GAM attention module to enhance feature extraction capabilities, improve semantic information and reduce interference; Finally, the rotating target detection module is added to make the detection frame fit the target contour better and improve the detection accuracy. The experimental results show that the average accuracy mean (mAP) of the YOLO-Rotating algorithm reaches 84.6% on the distribution safety equipment dataset, which is 2.5% higher than the original algorithm and the accuracy is improved by 2.07%. The algorithm has higher detection accuracy and better practical application value, meets the requirements of edge computing equipment, and is suitable for the construction scenario of power distribution live network operation.