LDA-YOLO: lightweight dynamic adjustment real-time detection power equipment model

作者：Hui Chen, Changsheng Zhu, Hongwei Bai, Tianyu Li, Jingjie Li, Yongxin Wang, Kexiang Liu, Zhiyuan Xia · 发表于：Engineering Research Express · 年份：2025 · DOI：10.1088/2631-8695/ae0f73 · 被引用次数：2 · 研究领域：Advanced Battery Technologies Research

Abstract Power system inspection is a key link to ensure its normal operation and stability. However, traditional detection models have weak generalization ability and usually only optimize for a single target, resulting in high detection costs and low efficiency. Models with strong generalization ability often have excessive computational overhead, making it difficult to meet the efficiency and real-time requirements of industrial applications. We propose a lightweight dynamic adjustable model LDA-YOLO based on YOLOv8-S to address the above issues. By systematically improving the model structure and feature processing mechanism, this method significantly enhances the model’s generalization and detection accuracy while maintaining its lightweight characteristics. Specifically, the improvement of the backbone is mainly achieved through the reconstruction of C2fDWCAA, which enhances the ability to capture local and global features without increasing the computational cost of the model. In addition, using GSConv and GSCDown instead of native downsampling effectively solves the significant computational overhead caused by native modules. For the Neck part of the module, the native concatenation module has poor ability to concatenate multi-scale information. We use CFCB for feature concatenation and improve the fusion effect of multi-scale features through context awareness. In addition, C2fCBB suppresses feature redundancy through low rank stage adaptive replacement. We also use ...