Detection of abnormal dangerous behavior in electrical power grids based on the fusion of contextual features
Xinyu Huang, Song Gao, Gang Qiu, Xiao Tan, Nailong Zhang, Jie Chen · Third International Conference on Computer Science and Communication Technology (ICCSCT 2022) · 2022
Intelligence inspection of the electrical power grid can effectively improve the efficiency of inspection. However, the complex scenes between grid work and the interference of similar objects seriously affect the recognition accuracy of target identification. To address the above problems, this paper proposes the detection of abnormal dangerous behaviors in power grids based on contextual feature fusion. First, the feature extraction backbone network is constructed to obtain image features. Second, the hierarchical contextual attention mechanism is constructed to capture contextual features. Finally, the target detection model with contextual feature fusion is constructed to achieve grid abnormal risk behavior detection. The model proposed in this article is compared with the existing object recognition model in the simulation of the three datasets of Safety helmet (hardhat) wearing detect dataset, Hard Hat Workers dataset, Safety Helmet Detection dataset. A large number of experiments have proved the object recognition algorithm proposed in this paper is effective and outperforms existing algorithms. The average recognition accuracy of the proposed model is 0.948, which is improved by 1.02%.