Network Parameter Configurations for a YOLOv11-Based UAV PV Detection Model

A set of network parameter configurations for a lightweight YOLOv11-based detection model designed for unmanned aerial vehicle (UAV) rooftop photovoltaic (PV) inspection. The dataset likely contains parameters describing the model architecture, such as layer configurations and module specifications. It was authored by Jiajun Zhu and last updated on May 6, 2026.

Use Cases

• Benchmarking lightweight detection models based on the reported precision, recall, and mAP metrics.
• Analyzing the trade-off between model size and performance based on the 2.04 M parameter count and 4.5 MB storage footprint.
• Studying the integration of attention mechanisms like Large Selective Kernel Attention (LSKBlock) for feature extraction in cluttered environments.
• Evaluating the use of feature reconstruction modules like G3Ghost for computational efficiency.

Strengths

• Model performance metrics are explicitly reported: precision of 96.32%, recall of 95.74%, and [email protected] of 98.5%.
• Model efficiency metrics are provided: only 2.04 M parameters, a 4.5 MB storage footprint, and an inference speed of 115 FPS.
• The dataset has a clear, specific application domain: UAV-based rooftop PV inspection.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is very small (5.5 KB), suggesting it contains only configuration parameters, not training or validation data.

Provenance

Source

figshare

Freshness

Last updated 2026-05-06 17:27:35; freshness should be verified.