ArticleSensors (Basel, Switzerland)2025
Research on Deep Learning-Based Multi-Level Cross-Domain Foreign Object Detection in Power Transmission Lines.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
With the rapid advancement of deep learning technology, deep learning-based methods have become the mainstream approach for detecting potential safety hazards in transmission lines, playing a crucial role in power grid safety monitoring. However, existing models are often overly complex and struggle with detecting small or occluded targets, limiting their effectiveness in edge-device deployment and real-time detection scenarios enhanced the YOLOv11 model by integrating it with the ConvNeXt network, a multi-level cross-domain analysis detection model (ConvNeXt-You Only Look Once) is proposed. Additionally, Bayesian optimization was employed to fine-tune the model's hyperparameters and accelerate convergence. Experimental results demonstrate that CO-YOLO mAP@0.5 reached 98.4%, mAP@0.5:0.95 reached 66.1%, and FPS was 303, outperforming YOLOv11 and ETLSH-YOLO, in both accuracy and efficiency. Compared with the original model, CO-YOLO model improved by 1.9% in mAP@0.5 and 2.2% in mAP@0.5:0.95.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.