AugFCOS: Augmented fully convolutional one-stage object detection network.

• A Robust Training Sample Selection strategy is proposed to effectively solve the problem of hyperparameter dependence. • Mitigate the impact of centerness loss without decreasing by designing a dynamic optimization loss. • A mixed attention module is proposed to enhance the multi-scale representation ability of feature pyramids. [Display omitted] As a pioneering work of introducing the idea of full convolutional network into the field of object detection, the fully convolutional one-stage object detection network (FCOS) has the advantage of excellent performance with low memory overhead. However, there are certain problems with FCOS that merit more research: the centerness quality assessment loss does not decrease during the late training stage, and its adaptive training sample selection (ATSS) relies heavily on the hyperparameter. To solve the aforementioned problems, we propose a novel object detection network, named augmented fully convolutional one-stage object detection network (AugFCOS). First of all, we propose an improved dynamic optimization loss (DOL) to mitigate the impact of the original centerness loss not decreasing. Then, a Robust Training Sample Selection (RTSS) is proposed to get rid of the dependence of hyper-parameter in ATSS of FCOS. Finally, a novel mixed attention feature pyramid network (MAFPN) is presented to enhance the multi-scale representation ability of feature pyramid network (FPN) and further improve the ability of multi-scale detection. The experimental results on MS COCO demonstrate the effectiveness of our proposed AugFCOS, where AugFCOS achieves approximate 2.0% to 2.9% increase when compared with ATSS and FCOS. [ABSTRACT FROM AUTHOR]