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September 5, 2025Scientific ReportsOpen Access

YOLOv8-MCDE for lightweight detection of small instruments in complex backgrounds from inspection robots’ perspective

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Authors

TYTongxin YangJiamusi University LDLing DingNingbo University QSQingwu Shi

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Implication

This algorithm improves small object detection performance and real-time efficiency for inspection robots, highlighting advances in model architecture and loss functions.

Key Points

YOLOv8-MCDE improves small object detection performance by integrating advanced model features.
The algorithm achieves a precision of 92.80% and mAP50 of 91.36%, showing significant enhancements over YOLOv8.
Using MobileNetV3 as a backbone, it reduces model complexity while enhancing operational efficiency.
Improvements in detection capability and computational resource demands make it ideal for inspection in complex environments.

Cite This Study

Yang et al. (2025) studied this question.

synapsesocial.com/papers/68bb49c46d6d5674bccffa3d https://doi.org/10.1038/s41598-025-17186-9

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Implication

This algorithm improves small object detection performance and real-time efficiency for inspection robots, highlighting advances in model architecture and loss functions.

YOLOv8-MCDE for lightweight detection of small instruments in complex backgrounds from inspection robots’ perspective

Discussion

Member takes

Implication

Key Points

Cite This Study

YOLOv8-MCDE for lightweight detection of small instruments in complex backgrounds from inspection robots’ perspective

Discussion

Member takes

Implication

Key Points

Cite This Study

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