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

Yuanping Shi, Yanheng Ma, Liang Geng, Lina Chu, Bingxuan Li, Wei Li

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In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Yuanping ShiDepartment of UAV Engineering, Shijiazhuang Campus, Army Engineering University, Shijiazhuang 050003, China.ORCID 0009-0004-7783-2291

Yanheng MaDepartment of UAV Engineering, Shijiazhuang Campus, Army Engineering University, Shijiazhuang 050003, China.

Liang GengCollege of Mechanical and Electrical Engineering, Shijiazhuang University, Shijiazhuang 050035, China.

Lina ChuDepartment of UAV Engineering, Shijiazhuang Campus, Army Engineering University, Shijiazhuang 050003, China.ORCID 0000-0001-8417-1226

Bingxuan LiDepartment of UAV Engineering, Shijiazhuang Campus, Army Engineering University, Shijiazhuang 050003, China.

Wei LiDepartment of UAV Engineering, Shijiazhuang Campus, Army Engineering University, Shijiazhuang 050003, China.ORCID 0009-0002-6228-3838

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Apple-detection performance in orchards degrades markedly under low-light conditions, where intensified noise and non-uniform exposure blur edge cues critical for precise localisation. We propose Knowledge Distillation with Geometry-Consistent Feature Alignment (KDFA), a compact end-to-end framework that couples image enhancement and detection through the following two complementary components: (i) Cross-Domain Mutual-Information-Bound Knowledge Distillation, which maximises an InfoNCE lower bound between daylight-teacher and low-light-student region embeddings; (ii) Geometry-Consistent Feature Alignment, which imposes Laplacian smoothness and bipartite graph correspondences across multiscale feature lattices. Trained on 1200 pixel-aligned bright/low-light image pairs, KDFA achieves 51.3% mean Average Precision (mAPQ [0.50:0.95]) on a challenging low-light apple-detection benchmark, setting a new state of the art by simultaneously bridging the illumination-domain gap and preserving geometric consistency.

Indexed as

feature alignmentknowledge distillationlow-light apple detection

Identifiers

PMID40808035

DOI10.3390/s25154871

PMCPMC12349361

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