🤖 AI Summary
This study addresses the lack of systematic validation regarding the effectiveness of large models for small object detection in resource-constrained Earth observation scenarios. Using rooftop photovoltaic detection in Madagascar as a case study, the authors systematically evaluate the impact of model scale, training data volume, and input resolution on detection efficiency. They report the first observation of an “efficiency inversion” phenomenon: the smallest model, YOLO11N, achieves both the highest mAP50 (0.617) and the greatest computational efficiency per unit cost. Moreover, high-resolution configurations of smaller models exhibit Pareto dominance across all deployment settings. Experimental results show that YOLO11N is 24 times more efficient than YOLO11X, increasing input resolution yields a 120% efficiency gain, and augmenting training data under low-resolution settings provides negligible benefits.
📝 Abstract
Scaling laws assume larger models trained on more data consistently outperform smaller ones -- an assumption that drives model selection in computer vision but remains untested in resource-constrained Earth observation (EO). We conduct a systematic efficiency analysis across three scaling dimensions: model size, dataset size, and input resolution, on rooftop PV detection in Madagascar. Optimizing for model efficiency (mAP$_{50}$ per unit of model size), we find a consistent efficiency inversion: YOLO11N achieves both the highest efficiency ($24\times$ higher than YOLO11X) and the highest absolute mAP$_{50}$ (0.617). Resolution is the dominant resource allocation lever ($+$120% efficiency gain), while additional data yields negligible returns at low resolution. These findings are robust to the deployment objective: small high-resolution configurations are Pareto-dominant across all 44 setups in the joint accuracy-throughput space, leaving no tradeoff to resolve. In data-scarce EO, bigger is not just unnecessary: it can be worse.