Abstract: Recently,object detection has gradually become a critical domain in the visual research community,while it’s also challenging to locate objects of different scales accurately for detectors.For the application of detecting objects in images,a high resolution anchor free(HOAR)detection strategy is proposed to meet the challenge of variable object scales.HOAR first inputs the image into a parallel high-resolution network with multiple paths(which is corresponding to different scales).Then the output feature map is extracted from each path as the deep feature representation of the image at each scale.Next,the dense feature pyramid(DenseFPN)is adopted to fuse the information of these feature maps to obtain the re-combined feature maps of multiple scales.Finally,the anchor-free detection sub-network is used to determine the object’s category and rectangle box position of each point on these feature maps.To verify the effectiveness of HOAR,comparative experiments are carried out on COCO dataset.First,the results on ablation study show the necessity of each module in the HOAR strategy.Second,the evaluation metric of detection,namely mAP,of HOAR reaches 40.5 on the validation set,which is significantly higher than mAP of all baseline models and some SOTA methods.In addition,the size of model parameters of HOAR strategy is significantly less than that of baseline models.