A Study on the Detection Method for Split Pin Defects in Power Transmission Lines Based on Two-Stage Detection and Mamba-YOLO-SPDC
Source abstract
Abstract
Detecting small split pins on transmission lines poses significant challenges, including low accuracy in complex backgrounds and slow inference speeds. To address these limitations, this study proposes a novel two-stage collaborative detection framework. The first stage utilizes a Yolo11x-based model to localize and crop components containing split pins from high-resolution images. This procedure transforms the difficult small-object detection problem into a more manageable, conventional detection task on a simplified background. For the second stage, a new high-performance detector, Mamba-YOLO-SPDC, is introduced. This model enhances the Yolo11 backbone by incorporating a Vision State Space (VSS) block, which leverages Mamba—a State Space Model (SSM) with linear computational complexity—to efficiently capture global context. Furthermore, a Space-to-Depth Convolution (SPD-Conv) module is integrated into the neck to mitigate the loss of fine-grained feature information during downsampling. Experimental results confirm the efficacy of the two-stage strategy. On the cropped dataset, the Mamba-YOLO-SPDC model achieves a mean Average Precision (mAP) of 61.9%, a 238% improvement over the 18.3% mAP obtained by the baseline Yolo11s on the original images. Compared to the conventional SAHI framework, the proposed method provides superior accuracy with a substantial increase in inference speed. This work demonstrates that the ‘localize first, then detect’ strategy, powered by the Mamba-YOLO-SPDC model, offers an effective balance between accuracy and efficiency for small object detection.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Vehicle License Plate Recognition · Advanced Measurement and Detection Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Electric Power Research Institute, Yunnan Power Grid Co., Ltd., China Southern Power Grid, Kunming 650220, China
Beijing Campus, North China Electric Power University, Beijing 102206, China
Baoding Campus, North China Electric Power University, Baoding 071003, China
Funding
Yunnan Fundamental Research Projects
References (24)
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