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Low-altitude aerial image power line foreign object detection method based on light convolutional neural network

A convolutional neural network and foreign object detection technology, applied in the field of deep learning, can solve problems such as the limited effect of target recognition methods, achieve the effect of ensuring real-time performance and improving detection efficiency

Active Publication Date: 2022-07-12
BEIJING UNIV OF TECH
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Problems solved by technology

[0005] Considering the increasing number of power line inspection images and the limited effect of traditional target recognition methods, this invention proposes a method for detecting foreign objects on power lines in aerial images based on multi-scale convolutional neural networks.

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  • Low-altitude aerial image power line foreign object detection method based on light convolutional neural network
  • Low-altitude aerial image power line foreign object detection method based on light convolutional neural network
  • Low-altitude aerial image power line foreign object detection method based on light convolutional neural network

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Embodiment Construction

[0021] According to the above description, the following is a specific implementation process, but the scope of protection of this patent is not limited to this implementation process.

[0022] Step 1: Construction of a power line foreign object detection model based on convolutional neural network

[0023] Step 1.1: Construction of Powerline Detection Model Based on Lightweight Convolutional Neural Network

[0024] The existing deep learning target detection models have a wide range of application scenarios and can often detect thousands of objects, such as YOLO9000, which can detect 9418 categories. In the power line inspection scenario, the types of targets are extremely limited, mainly power lines, balloons and kites. In this model, only power lines need to be identified, and their features are very limited. The existing deep learning target detection models are too specific for power line scenarios. Over-redundancy, while the lightweight model is effective in this case, ...

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Abstract

The detection method of power line foreign objects in low-altitude aerial imagery based on light convolutional neural network belongs to the field of computer vision. A real-time detection method for power line foreign objects in UAV aerial imagery is studied. First, a light-weight power line detection model is constructed by using convolutional neural network, and the depth features of power lines in aerial images are calculated. Then, a multi-target power line foreign body detection model is constructed by using convolutional neural network. Finally, the video frames without power lines are filtered by the power line detection model, and the multi-target power line foreign object detection model is used to realize real-time power line foreign object detection in low-altitude aerial images on the video with detected power lines.

Description

technical field [0001] Based on the deep learning technology, the present invention studies a real-time detection method for foreign objects in power lines in aerial photography images of unmanned aerial vehicles. First, a light-weight power line detection model is constructed by using convolutional neural network, and the depth features of power lines in aerial images are calculated. Then, a multi-target power line foreign body detection model is constructed by using convolutional neural network. Finally, the power line detection model is used to filter the video frames without power lines, and the multi-target power line foreign object detection model is used to realize real-time power line foreign object detection in low-altitude aerial images on the video with power lines detected. The invention belongs to the field of computer vision, and specifically relates to technologies such as deep learning and target detection. Background technique [0002] With the development ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V20/17G06N3/04G06N3/08
CPCG06N3/08G06V20/13G06N3/045Y04S10/50
Inventor 张菁王立元卓力梁西李昱钊
Owner BEIJING UNIV OF TECH
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