改进的Faster-RCNN目标检测方法在变电站悬挂异物检测中的应用

Application of an improved Faster-RCNN object detection method in the detection of suspended foreign matters in substation

针对变电站悬挂异物检测任务中异物形状多样、周围环境条件复杂,现有算法检测的准确率较低的问题,提出一种改进的Faster-RCNN目标检测方法,对变电站悬挂异物进行检测。将Faster-RCNN结合特征金字塔和可变性卷积,形成了改进的Faster-RCNN目标检测方法,扩展了Faster-RCNN网络结构对输入图片中不同尺度语义信息的读取,提升了网络对小目标的检测能力。采用了变电专业设备典型缺陷图像识别竞赛中的悬挂异物图像数据进行仿真实验,并与原有Faster-RCNN算法进行对比,实验结果验证了所提出方法的有效性,算法识别准确率得到提高,在真实样本中表现好,可有效应用于变电站巡检机器人系统中。

Aiming at the problems of various shapes of foreign matter,high complexity of surrounding environment and low accuracy of existing algorithm in the detection of suspended foreign objects in substations,this paper proposes an improved Faster-RCNN object detection method.Combining Faster-RCNN with feature pyramid networks and deformable convolutional networks,an improved Faster-RCNN object detection method is formed.The detection method strengthens the ability of Faster-RCNN to read semantic information of different scales in the input images,therefore improving its ability on detecting small objects.The image data is used in the image recognition model competition for typical defects of substation equipment,and the simulation experiment is carried out and compared with original Faster-RCNN.The experimental results verify the effectiveness of the proposed method.The improved algorithm,which has higher recognition accuracy and performs well on real detection samples,which can be effectively used in the substation inspection robot system.