Hybrid YOLOv8 and Fast R-CNN for Accurate Schematic Detection in Power Distribution Networks

Accurate schematic detection in Power Distribution Networks (PDNs) is critical for effective fault detection, asset management, and predictive maintenance. Conventional edge detection methods often struggle with the complexity and scale of modern PDNs, while standalone deep learning approaches face challenges in balancing real-time performance and high precision. To address these limitations, this paper introduces a hybrid detection framework that synergizes YOLOv8 and Fast R-CNN, leveraging their complementary strengths. YOLOv8 enables rapid initial detections with real-time capabilities, while Fast R-CNN refines these outputs to enhance contextual accuracy. A key contribution of this work is the integration of ensemble techniques—Soft Voting, Hard Voting, and Weighted Average Voting— which further optimize detection performance by effectively aggregating predictions from both models. Using a curated dataset of 3,304 schematic images, the proposed method achieves state-of-the-art results, including a precision of 96.59%, recall of 97.27%, and mean Average Precision at an Intersection over Union (IoU) threshold of 0.50 (mAP@50) of 99.06% with the Hard Voting ensemble. These findings underscore the robustness, scalability, and applicability of the proposed framework in automating schematic analysis. Furthermore, the method demonstrates strong potential for practical deployment within PLN Indonesia, particularly in real-time fault detection, technician training, and predictive maintenance, contributing to enhanced reliability and operational efficiency in national power distribution systems.