Runtime Comparison of DPCNet and YOLO11n for UAV Object Detection

A 5.5 KB XLS file contains runtime comparison data for the DPCNet and YOLO11n object detection models. The dataset, authored by Linfeng Jia and updated in March 2026, reports DPCNet's performance gains, including a 45% reduction in parameter count and [email protected] improvements of 2.0% and 5.1% on benchmark datasets.

Use Cases

• Compare [email protected] scores between DPCNet and the YOLO11n baseline across different UAV datasets.
• Analyze the reported 45% parameter count reduction to assess model efficiency trade-offs.
• Evaluate performance metrics for small, dense, and occluded targets as highlighted in the model comparison.

Strengths

• Provides specific performance metrics, including [email protected] improvements of 2.0% and 5.1%.
• Cites a concrete model efficiency fact: a 45% reduction in parameter count for DPCNet.
• Data is recent, with a last update timestamp of March 13, 2026.

Limitations

• The dataset is extremely small at 5.5 KB, indicating very limited scope and likely a summary table, not raw experimental data.
• No row or column details are provided, preventing assessment of data granularity or structure.
• Performance claims are presented as final results without underlying raw detection outputs for independent verification.

Provenance

Source

Linfeng Jia via figshare.

Collection Method

Derived from experiments on VisDrone2019 and HIT-UAV benchmark datasets.

Time Range

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Freshness

Last updated 2026-03-13.

Geography

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