  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.

Computer Vision and Pattern Recognition