Abstract: An enhanced FCOS object detection algorithm is proposed to address the problems that the classical anchorless frame object detection algorithm FCOS(fully constitutional one-stage object detection) has difficulty in extracting target information,insufficient ability to combine location and content information,and weak performance due to insufficient differentiation between positive and negative sample.The method first adds a deformable convolution module and a global attention module to the ResNet50 feature extraction network to improve the feature information capture capability.Then,the FPN feature pyramid is combined with the deep link layer to form a multi-scale feature fusion module to improve the feature extraction effect.Finally,the adaptive division of positive and negative samples module is added to enhance the accuracy of the test frame to achieve the effect of improving the regression accuracy.In order to test the detection effect of the algorithm,the COCO dataset and VOC dataset are used for experiments.Compared with the original FCOS algorithm,the average accuracy of the proposed algorithm on the two datasets improves by 2.3% and 1.8%,respectively.Among them,there is a significant improvement for the detection of small targets in the COCO dataset.

Key words: Target detection, Deformable convolution, Global attention, Multi-scale features, Feature pyramid, Positive and negative samples