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Related Experiment Videos

MSCSM-YOLOv11s: multi-scale feature extraction and CSM attention for small target detection.

Shuting Huang1, Juan Guo2

  • 1Guilin University of Electronic Technology, Guilin, China.

Scientific Reports
|July 1, 2026
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...

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A new MSCSM-YOLOv11s model improves small-target detection in UAV imagery by enhancing feature extraction and preserving details. This framework offers better precision and recall for aerial object identification.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Remote Sensing

Background:

  • Small-target detection in aerial images is difficult due to low resolution, scale variation, and complex backgrounds.
  • Existing methods struggle with preserving fine-grained features during downsampling.

Purpose of the Study:

  • To introduce MSCSM-YOLOv11s, an enhanced YOLOv11s framework for improved small-target detection in Unmanned Aerial Vehicle (UAV) imagery.
  • To address challenges like low resolution, occlusion, and feature loss in aerial small-object detection.

Main Methods:

  • Developed a Multi-Scale Dilated Convolution (MSDC) module to enlarge receptive fields and extract multi-scale features while retaining local details.
  • Incorporated a Channel-Spatial Module (CSM) to strengthen feature representation by modeling channel and spatial information for enhanced robustness.
Keywords:
Channel-spatial attentionMulti-scale feature extractionSmall-target detectionUAV imageryVisDrone2019YOLOv11s

Related Experiment Videos

  • Added an extra small-object detection layer to preserve shallow spatial features and minimize information loss during downsampling.

    • Main Results:

    • MSCSM-YOLOv11s achieved a precision of 56.1%, recall of 42.7%, mAP@0.5 of 45.5%, and mAP@0.5:0.95 of 27.8% on the VisDrone-2019 dataset.
    • The proposed model outperformed the baseline YOLOv11s by significant margins across all evaluated metrics.
    • Ablation studies confirmed the effectiveness of the MSDC, CSM, and the additional detection layer.

    Conclusions:

    • The MSCSM-YOLOv11s framework offers a more competitive performance for small-target detection in UAV imagery.
    • The model effectively enhances feature extraction and preserves crucial details, leading to improved detection accuracy.
    • The proposed approach balances high detection performance with acceptable computational efficiency.