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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.
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Related Experiment Video

Updated: May 28, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

EfficientIR-Det Towards Efficient and Accurate DETR for UAV Infrared Object Detection.

Xiang Yang1,2, Hanbin Li1, Xiaolan Xie1,2

  • 1College of Computer Science and Engineering, Guilin University of Technology, Guilin 541006, China.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary

EfficientIR-Det enhances infrared object detection for unmanned aerial vehicles (UAVs) by optimizing backbone, encoder, and sampling. This lightweight detector achieves superior performance with reduced computational cost for real-time edge applications.

Keywords:
DETRUAVend-to-end detectioninfrared object detectionstate space model

Related Experiment Videos

Last Updated: May 28, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Infrared (IR) object detection on unmanned aerial vehicles (UAVs) faces challenges from low signal-to-noise ratios and limited onboard computational power.
  • Existing Convolutional Neural Networks (CNNs) lack global context, while Transformers exhibit quadratic complexity, impeding real-time deployment.

Purpose of the Study:

  • To develop a lightweight, end-to-end infrared object detector for UAVs that overcomes computational and performance bottlenecks.
  • To introduce novel architectural components for efficient feature extraction and global context modeling in resource-constrained environments.

Main Methods:

  • Proposed EfficientIR-Det, a holistic detector optimizing backbone, encoder, and sampling.
  • Introduced Partial Star Network (PSN) backbone for implicit high-dimensional feature expansion and weak IR signal amplification.
  • Developed Hierarchical Mamba (HiMamba) encoder for linear-complexity global enhancement and Adaptive Gated Sampling (AGS) with Hierarchical Sampling Strategy (HSS) for refined cross-scale representation.

Main Results:

  • Achieved 88.4% mAP@0.5 on HIT-UAV, surpassing RT-DETR-R18 by 3.3 points.
  • Reduced FLOPs by 48.9% and parameters by 44.2% compared to baseline.
  • Attained 74.1% mAP@0.5 and 140.8 FPS on the DroneVehicle dataset.

Conclusions:

  • EfficientIR-Det offers a promising solution for robust, real-time infrared perception on edge-constrained UAV platforms.
  • The proposed methods demonstrate significant improvements in detection accuracy and computational efficiency for UAV-based IR object detection.