No abstract

Aiming at recognizing small proportion, blurred and complex traffic sign in natural scenes, a traffic sign detection method based on RetinaNet-NeXt is proposed. First, to ensure the quality of dataset, the data were cleaned and enhanced to denoise. Secondly, a novel backbone network ResNeXt was employed to improve the detection accuracy and effection of RetinaNet. Finally, transfer learning and group normalization were adopted to accelerate our network training. Experimental results show that the precision, recall and mAP of our method, compared with the original RetinaNet, are improved by 9.08%, 9.09% and 7.32%, respectively. Our method can be effectively applied to traffic sign detection.

Purpose Corona virus disease 2019 (COVID‐19) is threatening the health of the global people and bringing great losses to our economy and society. However, computed tomography (CT) image segmentation can make clinicians quickly identify the COVID‐19‐infected regions. Accurate segmentation infection area of COVID‐19 can contribute screen confirmed cases. Methods We designed a segmentation network for COVID‐19‐infected regions in CT images. To begin with, multilayered features were extracted by the backbone network of Res2Net. Subsequently, edge features of the infected regions in the low‐level feature f 2 were extracted by the edge attention module. Second, we carefully designed the structure of the attention position module (APM) to extract high‐level feature f 5 and detect infected regions. Finally, we proposed a context exploration module consisting of two parallel explore blocks, which can remove some false positives and false negatives to reach more accurate segmentation results. Results Experimental results show that, on the public COVID‐19 dataset, the Dice, sensitivity, specificity, , , and mean absolute error ( MAE ) of our method are 0.755, 0.751, 0.959, 0.795, 0.919, and 0.060, respectively. Compared with the latest COVID‐19 segmentation model Inf‐Net, the Dice similarity coefficient of our model has increased by 7.3%; the sensitivity (Sen) has increased by 5.9%. On contrary, the MAE has dropped by 2.2%. Conclusions Our method performs well on COVID‐19 CT image segmentation. We also find that our method is so portable that can be suitable for various current popular networks. In a word, our method can help screen people infected with COVID‐19 effectively and save the labor power of clinicians and radiologists.

No abstract