GP-YOLO: Ablation Experiment Results for Insulator Defect Detection

Ablation experiment results for the RDA-YOLO algorithm, a high-precision model for detecting defects in power grid insulators from drone imagery. The dataset, shared by Xiaoxiong Zhou on figshare, contains results demonstrating the model's 91.6% precision and 91.4% mAP0.5 performance. The data was last updated on 2026-05-15.

Use Cases

• Benchmarking object detection model performance based on reported precision and mAP metrics
• Analyzing the impact of architectural changes like the Reverse Large-Selection Kernel module on model accuracy
• Evaluating model robustness for industrial inspection tasks under extreme weather conditions as mentioned in the description

Strengths

• Dataset is small (5.5 KB), facilitating quick download and analysis
• Results include specific performance metrics: 91.6% precision and 91.4% mAP0.5
• Data is shared under a permissive CC-BY-4.0 license

Limitations

• Row count is unknown, which may limit suitability assessment
• Column-level documentation is absent; field semantics must be inferred after download
• The proprietary nature of the underlying image dataset limits reproducibility

Provenance

Source

Xiaoxiong Zhou via figshare

Collection Method

Results from experiments on a proprietary insulator defect image dataset

Freshness

Last updated 2026-05-15 17:35:34