基于改进Faster-RCNN的输电线巡检图像多目标检测及定位

针对输电线巡检图像受光线、环境和拍摄角度等因素影响,图像中的电气设备呈现低分辨率和多形态化特征的问题,提出一种基于改进Faster-RCNN的巡检图像多目标检测及定位方法。该方法首先通过区域建议策略网络生成若干目标候选区域;然后基于实际巡检图像样本库,对卷积神经网络进行训练,以改善参数学习效果;最后利用正则化方法优化参数权重,提高检测速度,得到适应巡检图像多形态化特征的改进型Faster-RCNN模型。实际场景数据集测试结果表明,相比于数字图像处理、浅层机器学习、单阶法、双阶法、Mask-RCNN和Local Loss目标检测方法,所提改进型Faster-RCNN能够在不同分辨率和不同位置角度的巡检图像场景下保持较高的识别精度和速度,具有较高的工程实用价值。

Multi-target detection and location of transmission line inspection image based on improved Faster-RCNN

Because the transmission line inspection images are influenced by light, environment, shooting angles and so on, the electrical equipment in the image presents low resolution and polymorphic characteristics. Aiming at this problem, a multi-target detection and location of transmission line inspection image based on improved Faster-RCNN(Faster-Region-Convolutional Neural Network) is proposed. Firstly, a number of target candidate regions are gene-rated by using region proposal network. Then, the CNN(Convolution Neural Network) is trained based on the actual inspection image sample database to improve the parameter learning effect. Finally, the regularization method is used to optimize the parameter weights and improve the detection speed, then the improved Faster-RCNN model suitable for polymorphic features of inspection images is established. The test results of actual scene datasets show that compared with the digital image processing, shallow machine learning, single-stage method, double-stage method, Mask-RCNN and Local Loss target detection method, the proposed improved Faster-RCNN maintains a higher recognition accuracy and speed in inspection images of different resolution and angle, and it has high engineering value.