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This paper proposes an efficient, low-complexity and anchor-free objectdetector based on the state-of-the-art YOLO framework, which can be implementedin real time on edge computing platforms. We develop an enhanced dataaugmentation method to effectively suppress overfitting during training, anddesign a hybrid random loss function to improve the detection accuracy of smallobjects. Inspired by FCOS, a lighter and more efficient decoupled head isproposed, and its inference speed can be improved with little loss ofprecision. 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-DETdataset, and it meets real-time requirements (FPS>=30) on edge-computing deviceNvidia Jetson AGX Xavier. We also designed lighter models with less parametersfor edge computing devices with lower computing power, which also show betterperformances. Our source code, hyper-parameters and model weights are allavailable at https://github.com/LSH9832/edgeyolo.

EdgeYOLO: An Edge-Real-Time Object Detector