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

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 DySample to replace native downsampling, enhancing the real-time monitoring efficiency of the model. Finally, in the detection head section, we use the DyHead detection head to achieve dynamic adjustability of the model. Experimental results demonstrate that LDA-YOLO achieves 31.8% and 23.2% reductions in parameters and computational complexity respectively compared to the baseline model. The model attains <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>A</mml:mi> <mml:msub> <mml:mrow> <mml:mi>P</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>I</mml:mi> <mml:mi>o</mml:mi> <mml:mi>U</mml:mi> <mml:mo>=</mml:mo> <mml:mn>50</mml:mn> </mml:mrow> </mml:msub> </mml:math> scores of 76.8, 62.2, 81.4, and 75.6 on power line, insulator, foreign object, and equipment detection tasks, validating its superior performance in complex scenarios. The implementation is publicly available at https://github.com/chenhui331/LDA-YOLO .