Author(s)

Liang Liu¹, Nan Yang¹, Saifei Liu¹, Yuanyuan Cao¹, Shuowen Tian¹, Tiancheng Liu¹, Xun Zhao²

¹Rizhao Power Supply Company, State Grid Shandong Electric Power Company, Rizhao, China.
²School of Automation, Beijing Information Science and Technology University, Beijing, China.

Aiming at the problem of low accuracy of traditional target detection methods for target detection in endoscopes in substation environments, a CNN-based real-time detection method for masked targets is proposed. The method adopts the overall design of backbone network, detection network and algorithmic parameter optimisation method, completes the model training on the self-constructed occlusion target dataset, and adopts the multi-scale perception method for target detection. The HNM algorithm is used to screen positive and negative samples during the training process, and the NMS algorithm is used to post-process the prediction results during the detection process to improve the detection efficiency. After experimental validation, the obtained model has the multi-class average predicted value (mAP) of the dataset. It has general advantages over traditional target detection methods. The detection time of a single target on FDDB dataset is 39 ms, which can meet the need of real-time target detection. In addition, the project team has successfully deployed the method into substations and put it into use in many places in Beijing, which is important for achieving the anomaly of occlusion target detection.

Real-Time Mask Target, CNN (Convolutional Neural Network), Single-Stage Detection, Multi-Scale Feature Perception

Liu, L. , Yang, N. , Liu, S. , Cao, Y. , Tian, S. , Liu, T. and Zhao, X. (2024) A CNN-Based Single-Stage Occlusion Real-Time Target Detection Method. Journal of Intelligent Learning Systems and Applications, 16, 1-11. doi: 10.4236/jilsa.2024.161001.