SDG-CSNet: a detection network based on spatial detail guidance and clue screening for substation equipment

Abstract Substation equipment carries critical operational information. For the intelligent monitoring of power systems, the deployment of camera-equipped wheeled robots and unmanned aerial vehicles 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 an efficient clue-scanning module (ECSM), visual detail retention (VDR), and a detail-guided path aggregation network (DG-PAN). To tackle the degradation of image quality induced by complex illumination conditions, the input images undergo edge detail smoothing via preprocessing, while the ECSM utilizes a 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 a state-of-the-art performance with 57.3% mAP. The detection speed, model size, and computation are 59.9 frames per second, 6.25 M, and 14.1 G. 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.