Abstract
In order to accurately identify targets such as insulators, shock hammers, bird nests, and spacers on high-voltage transmission lines, this paper proposes a multitarget detection model for transmission lines based on DANet and YOLOv4. First, the DANet and YOLOv4 are fused to solve the difficulty in understanding the scene and the discrimination of pixels caused by the complex and diverse scenes of UAV’ (unmanned aerial vehicle) aerial images (lighting, viewing angle, scale, occlusion, and so on) so as to improve the significance of the detection target. Gaussian function and KL (Kullback–Leibler) divergence are used to improve the nonmaximum suppression in YOLOv4 so as to improve the recognition rate of occluded targets; the focal loss function and the balanced cross entropy function are used to improve the loss function of YOLOv4 in order to reduce the impact of not only the imbalance between the background and the detection target but also the imbalance among the samples, which is aimed at improving the accuracy of the detection. Then, a data set is made for the experiment by using the UAV inspection image provided by a power grid company in Eastern Inner Mongolia. Finally, the algorithm proposed in this paper is compared with other target detection algorithms. Experimental results show that the average detection accuracy of the proposed algorithm can reach 94.7%, and the detection time of each image is 0.05 seconds. The method has good accuracy, real-time, and robustness.
Highlights
In the actual transmission line inspection, the background of drone aerial images is complex, the light intensity is different, and some targets are blocked. erefore, there are certain difficulties in accurately identifying the targets of the transmission line, which need to be resolved
E first step is to label the aerial image taken during the drone inspection with labelImg and adjust the input size of the image to 608 × 608. e second step is to input the processed pictures into the improved YOLOv4 network for training and perform multiple rounds of training to obtain the training weights of the transmission line insulator detection model. e final step is using the test set to verify this model
DANet adopts spatial attention module and channel attention module, which establish semantic interdependence of spatial dimension and channel dimension, respectively, and makes use of feature information in global view, which is crucial for scene segmentation and can more accurately distinguish targets of different categories. erefore, the fusion of DANet in YOLOv4 can greatly improve the saliency of insulators, shock hammers, spacers, and bird nests in complex background, improving the accuracy of identification. e experimental data in Table 1 prove that DANet can improve the average accuracy of the model
Summary
Modules, respectively, to capture global semantic information (some relationship established between pixels). We feed feature A into a convolution layer to generate a new feature map D ∈ RC×H×W and reshape it to RC×N. en, we perform a matrix multiplication between D and the transpose of S and reshape the result to RC×H×W. We multiply it by a scale parameter α and perform an element-wise sum operation with the features A to obtain the final output E ∈ RC×H×W as follows: N. i 1 where Di is the ith column and α is the training parameter.
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