SDG-CSNet: A Detection Network Based on Spatial Detail Guidance and Clue Screening for Substation Equipment

Substation equipment carrying critical operational information. For intelligent monitoring of power systems, the deployment of camera-equipped wheeled robots and unmanned aerial vehicles (UAVs) for image acquisition and real-time detection of substation equipment represents an essential routine inspection methodology. While modern detection algorithms have achieved widespread adoption in power industry applications, state-of-the-art methods still face significant performance degradation when handling dense small-target detection tasks in complex field environments. To address these limitations, this paper proposes a novel Spatial Detail-Guided and Clue-Screening Network (SDG-CSNet), including Efficient Clues Scanning Module (ECSM), Visual Details Retention (VDR) and Detail-Guided Path Aggregation Network (DG-PAN). To tackle the degradation of image quality induced by complex illumination conditions, the input images undergoes edge detail smoothing via preprocessing, while ECSM utilizes rescreening mechanism to effectively integrate local connectivity with global receptive fields for feature reconstruction. Addressing the characteristics of densely distributed and small-sized objects, VDR employs pixel-adaptive downsampling strategies to maintain high-frequency information. Moreover, DG-PAN implements delayed multi-scale fusion of shallow-level features, thereby guiding feature maps to adjust dynamically. Experiments demonstrate that SDG-CSNet achieves state-of-the-art performance with 57.3\% mAP. The detection speed, model size, and computation are 59.9 frames per second (FPS), 6.25M, and 14.1G. SDG-CSNet demonstrates superior robustness in low-light and motion-blur conditions. This work provides a practical and efficient solution for substation equipment detecting applications.