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<p>Object detection in aerial imagery faces significant challenges from small, randomly oriented, and crowded targets across large frames, where default hyperparameter settings consistently underperform. This paper presents a systematic methodology for hyperparameter optimization of YOLO (You Only Look Once) through a novel integration of Differential Evolution, Multi-fidelity Optimization, and Bayesian Optimization (DE-MFO-BO). Our approach optimizes four critical hyperparameters; learning rate, batch size, momentum, and weight decay for both Adam and SGD (Stochastic Gradient Descent) optimizers. The methodology employs a two-stage strategy, Differential Evolution performs rapid exploration using low-fidelity training (5 epochs) to identify promising hyperparameter regions, followed by Bayesian Optimization with high-fidelity training (up to 800 epochs) for precise refinement. A composite fitness function combining precision, recall, and mean Average Precision (IoU 50–95) (mAP(50–95)) guides the optimization process. We validate this framework on VisDrone2019 and our indigenous Realm dataset using YOLO-11n model. For VisDrone2019, Adam optimizer achieves 18.42% recall improvement and 13.5% mAP(50–95) enhancement, while SGD shows 5.07% precision increase. On the Realm dataset, Adam optimizer demonstrates remarkable gains with 20.96% mAP(50–95) improvement, 10.22% precision increase, and 7.17% recall enhancement, whereas SGD achieves 2.78% precision improvement. These substantial performance improvements, achieved without architectural modifications, establish the effectiveness of our systematic hyperparameter optimization approach for aerial object detection applications.</p>

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Hyperparameter optimization of YOLO using differential evolution, multi-fidelity optimization, and Bayesian optimization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月10日

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  • Muhammad Uzair Gill,
  • Parvathy Rajendran

摘要

Object detection in aerial imagery faces significant challenges from small, randomly oriented, and crowded targets across large frames, where default hyperparameter settings consistently underperform. This paper presents a systematic methodology for hyperparameter optimization of YOLO (You Only Look Once) through a novel integration of Differential Evolution, Multi-fidelity Optimization, and Bayesian Optimization (DE-MFO-BO). Our approach optimizes four critical hyperparameters; learning rate, batch size, momentum, and weight decay for both Adam and SGD (Stochastic Gradient Descent) optimizers. The methodology employs a two-stage strategy, Differential Evolution performs rapid exploration using low-fidelity training (5 epochs) to identify promising hyperparameter regions, followed by Bayesian Optimization with high-fidelity training (up to 800 epochs) for precise refinement. A composite fitness function combining precision, recall, and mean Average Precision (IoU 50–95) (mAP(50–95)) guides the optimization process. We validate this framework on VisDrone2019 and our indigenous Realm dataset using YOLO-11n model. For VisDrone2019, Adam optimizer achieves 18.42% recall improvement and 13.5% mAP(50–95) enhancement, while SGD shows 5.07% precision increase. On the Realm dataset, Adam optimizer demonstrates remarkable gains with 20.96% mAP(50–95) improvement, 10.22% precision increase, and 7.17% recall enhancement, whereas SGD achieves 2.78% precision improvement. These substantial performance improvements, achieved without architectural modifications, establish the effectiveness of our systematic hyperparameter optimization approach for aerial object detection applications.