This paper proposes an efficient, low-complexity and anchor-free object detector based on the state-of-the-art YOLO framework, which can be implemented in real time on edge computing platforms. We develop an enhanced data augmentation method to effectively suppress overfitting during training, and design a hybrid random loss function to improve the detection accuracy of small objects. Inspired by FCOS, a lighter and more efficient decoupled head is proposed, and its inference speed can be improved with little loss of precision. Our baseline model can reach the accuracy of 50.6% AP50:95 and 69.8% AP50 in MS COCO2017 dataset, 26.4% AP50:95 and 44.8% AP50 in VisDrone2019-DET dataset, and it meets real-time requirements (FPS>=30) on edge-computing device Nvidia Jetson AGX Xavier. We also designed lighter models with less parameters for edge computing devices with lower computing power, which also show better performances. Our source code, hyper-parameters and model weights are all available at https://github.com/LSH9832/edgeyolo.

翻译：本文提出了一种基于前沿YOLO框架的高效、低复杂度且无锚点的目标检测器，可在边缘计算平台上实现实时部署。我们开发了一种增强型数据扩充方法，有效抑制训练过程中的过拟合现象，并设计了混合随机损失函数以提高小目标检测精度。受FCOS启发，本文提出了更轻量且高效的解耦检测头，在精度损失极小的前提下提升了推理速度。我们的基线模型在MS COCO2017数据集上达到50.6%的AP50:95和69.8%的AP50，在VisDrone2019-DET数据集上达到26.4%的AP50:95和44.8%的AP50，并在边缘计算设备Nvidia Jetson AGX Xavier上满足实时性要求（FPS>=30）。针对算力较低的边缘计算设备，我们还设计了参数更少的轻量级模型，同样展现出更优性能。本研究的源代码、超参数及模型权重均已开源至 https://github.com/LSH9832/edgeyolo。