DSE-YOLO11: Experimental Environment Configuration for Road Object Detection

5.5 KB of experimental configuration data supports a proposed lightweight object detector, DSE-YOLO11, designed for complex road scenes. The dataset, authored by Yange Chen and last updated in June 2026, contains results showing the model improves recall from 0.744 to 0.811 and mAP50 from 0.810 to 0.856 while maintaining 2.96M parameters and 7.1 GFLOPs.

Use Cases

• Benchmarking lightweight object detection models based on reported recall and mAP50 metrics.
• Analyzing model performance trade-offs for small and low-contrast traffic elements based on the described detection challenges.
• Reproducing experimental configurations for road scene object detection based on the described integration of DynamicConv, SlimNeck, and EMA modules.

Strengths

• Performance metrics are explicitly quantified, showing recall improved from 0.744 to 0.811 and mAP50 from 0.810 to 0.856.
• Model complexity is specified with 2.96M parameters and 7.1 GFLOPs.
• The dataset is licensed under CC-BY-4.0 for open use.

Limitations

• The dataset is very small at 5.5 KB, suggesting limited scope.
• Row count and column-level documentation are absent; field semantics must be inferred after download.
• The description focuses on model performance; the exact content of the XLS file is unspecified.

Provenance

Source

Yange Chen via figshare.

Collection Method

Likely contains experimental configuration and results for the proposed DSE-YOLO11 model.

Freshness

Last updated 2026-06-01 17:34:26; freshness should be verified.