SD-FINE: Lightweight Object Detection Method for Critical Equipment in Substations

The safe and stable operation of critical substation equipment is paramount to the power system, and its intelligent inspection relies on highly efficient and accurate object detection technology. However, the demanding requirements for both accuracy and efficiency in complex environments pose significant challenges for lightweight models. To address this, this paper proposes SD-FINE, a lightweight object detection technique specifically designed for detecting critical substation equipment. Specifically, we introduce a novel Fine-grained Distribution Refinement (FDR) approach, which fundamentally transforms the bounding box regression process in DETR from predicting coordinates to iteratively optimizing edge probability distributions. Central to the new FDR is an adaptive weight function learning mechanism that learns weights for these distributions. This mechanism is designed to enhance the model’s perception capability regarding equipment location information within complex substation environments. Additionally, this paper develops a new Efficient Hybrid Encoder that provides adaptive scale weighting for feature information at different scales during cross-scale feature fusion, enabling more flexible and efficient lightweight feature extraction. Experimental validation on a critical substation equipment detection dataset demonstrates that SD-FINE achieves an accuracy of 93.1% while maintaining model lightness. It outperforms mainstream object detection networks across various metrics, providing an efficient and reliable detection solution for intelligent substation inspection.