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Knowledge Distillation with Geometry-Consistent Feature Alignment for Robust Low-Light Apple Detection.

Yuanping Shi^1,2,3, Yanheng Ma¹, Liang Geng^2,3

¹Department of UAV Engineering, Shijiazhuang Campus, Army Engineering University, Shijiazhuang 050003, China.

Sensors (Basel, Switzerland)

|August 14, 2025

Summary

This summary is machine-generated.

This study introduces a new framework, Knowledge Distillation with Geometry-Consistent Feature Alignment (KDFA), to improve low-light apple detection. KDFA enhances image quality and detection accuracy in challenging orchard conditions.

Keywords:

feature alignment knowledge distillation low-light apple detection

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Area of Science:

Computer Vision
Machine Learning
Agricultural Technology

Background:

Low-light conditions significantly degrade apple detection accuracy in orchards due to noise and non-uniform exposure.
Edge cues essential for precise localization are blurred, hindering automated harvesting and monitoring systems.

Purpose of the Study:

To develop a compact, end-to-end framework for robust apple detection under low-light conditions.
To bridge the illumination domain gap while preserving critical geometric information for improved localization.

Main Methods:

Proposes Knowledge Distillation with Geometry-Consistent Feature Alignment (KDFA), integrating image enhancement and detection.
Employs Cross-Domain Mutual-Information-Bound Knowledge Distillation to align features between daylight and low-light images.
Utilizes Geometry-Consistent Feature Alignment with Laplacian smoothness and bipartite graph correspondences across feature lattices.

Main Results:

Achieved 51.3% mean Average Precision (mAP) on a challenging low-light apple detection benchmark.
Set a new state-of-the-art performance, outperforming existing methods in low-light scenarios.
Demonstrated effective bridging of the illumination domain gap and preservation of geometric consistency.

Conclusions:

KDFA framework significantly improves apple detection performance in low-light orchard environments.
The proposed method offers a robust solution for precision agriculture applications requiring accurate object localization under adverse lighting.
Future work can explore adaptability to other agricultural object detection tasks and varying illumination conditions.