Optimizing long-range UAV detection on YOLOv8: Breaking-point distance analysis and combining adaptive tiling with AdamW optimizer
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https://doi.org/10.54939/1859-1043.j.mst.109.2026.154-163Keywords:
UAV; Small object detection; YOLOv8; Image tiling; AdamW; Breaking point; Computer vision.Abstract
The rapid proliferation of unmanned aerial vehicles (UAVs) has imposed stringent requirements on surveillance and early warning systems. In long-range detection scenarios, the apparent size of UAVs in images decreases significantly, leading to severe spatial information loss and degraded performance of convolutional neural network (CNN)-based detection models. This paper proposes a continuous quantitative analysis framework to model the relationship between observation distance and UAV detection performance by progressively reducing the input image resolution. Based on experimental regression analysis, a system-level breaking point is identified, representing a distance threshold at which detection performance begins to degrade sharply and exhibits nonlinear behavior. Furthermore, a solution integrating adaptive image tiling with the AdamW optimizer is proposed to ensure training stability and enhance performance in long-range scenarios. Experimental results on the YOLOv8s model show that the proposed approach improves mAP@0.5 in long-range detection by up to +24.9% while eliminating numerical instability during training on tiled data. Regression analysis identifies the system-level breaking point at Dc ≈ 2.5, providing a quantitative basis for activating adaptive image processing in real-world deployments on resource-constrained platforms.
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