输配电线路危险目标互补注意力深度学习检测

鸟巢是造成输电线路相间短路的重要因素之一,在智能监测设备采集的图像中,鸟巢目标轮廓尺度跨度大、成像背景复杂多变,鸟巢目标的自动检测面临着很大的挑战。针对这种难题,提出了一种互补注意力深度学习检测模型(Complementation Attention Network Deep Learning Detector,CANDL-Detecor)。首先,设计了新颖的互补注意力网络模块,由通道注意力和空间注意力并联而成,充分利用通道特征和空间位置特征的互补优势,自适应地抑制背景噪声特征,同时突出鸟巢特征。接着,设计了互补注意力深度学习检测模型,融入了一阶段特征金字塔池化子网络,从而引导深度神经网络在突出目标特征的同时抑制复杂背景的干扰,提高了鸟巢多尺度特征的表示能力。实验结果和分析证实了提出方法的有效性,均值平均精度(MAP)达到88.2%,检测速度达到98fps。

Small objects detection in UAV aerial images based on improved Faster R-CNN

In order to solve the problem of small objects detection in unmanned aerial vehicle(UAV)aerial images with complex background,a general detection method for multi-scale small objects based on Faster region-based convolutional neural network(Faster R-CNN)is proposed.The bird’s nest on the high-voltage tower is taken as the research object.Firstly,we use the improved convolutional neural network ResNet101 to extract object features,and then use multi-scale sliding windows to obtain the object region proposals on the convolution feature maps with different resolutions.Finally,a deconvolution operation is added to further enhance the selected feature map with higher resolution,and then it taken as a feature mapping layer of the region proposals passing to the object detection sub-network.The detection results of the bird’s nest in UAV aerial images show that the proposed method can precisely detect small objects in aerial images.