[Submitted on 24 Sep 2026]
Title:CSCWD: Cross-Scale Channel-wise Knowledge Distillation for Lightweight Tiny Object Detection on Edge Devices
View a PDF of the paper titled CSCWD: Cross-Scale Channel-wise Knowledge Distillation for Lightweight Tiny Object Detection on Edge Devices, by Amir Zamani and 1 other authors
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Abstract:Real-time tiny object detection in aerial imagery is constrained by the weak spatial evidence of very small objects and the loss of high-resolution detail in lightweight detectors. This study presents Cross-Scale Channel-wise Knowledge Distillation (CSCWD), a training-time framework that transfers high-resolution spatial representations from a YOLO11m-P2 teacher to a compact YOLO11n student without altering the student's inference architecture. Unlike conventional same-scale feature distillation, CSCWD transfers supervision from teacher P2 to student P3 after feature alignment while retaining same-scale distillation at deeper pyramid levels. Under the unified seven-sequence Drone-vs-Bird validation protocol, YOLO11n-CSCWD achieves 50.17% mean average precision at an intersection-over-union threshold of 0.5 ([email protected]) and 59.73% recall, improving the matched CA-YOLO11n baseline by 2.92 percentage points in [email protected] and 3.55 points in recall. Cross-scale alignment further increases [email protected] by 2.09 points over the corresponding same-scale channel-wise distillation configuration. In zero-shot evaluation on DUT-Anti-UAV, [email protected] increases from 48.29% to 50.06% without target-domain fine-tuning. This domain was included because its challenging small targets make low-latency, computationally efficient detection particularly relevant. On Raspberry Pi 5 using NCNN-FP16 at 640x640 resolution, the 2.58-million-parameter student achieves 50.32% [email protected] at 82.32 ms mean wall-clock latency, or 12.15 frames per second, while retaining essentially the same runtime and memory requirements as the matched baseline. The results support cross-scale distillation for improving tiny-target detection without increasing inference-time model complexity.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.30395 [cs.CV]
(or arXiv:2609.30395v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.30395
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Amir Zamani [view email] [v1] Thu, 24 Sep 2026 18:02:25 UTC (12,600 KB)
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