A D-G-YOLOV3 algorithm was proposed to identify and judge recyclables,which introduced a dense feature network to replace the feature pyramid network.The network closely connects and fits the feature maps and simulate...A D-G-YOLOV3 algorithm was proposed to identify and judge recyclables,which introduced a dense feature network to replace the feature pyramid network.The network closely connects and fits the feature maps and simulates human judgment mechanism.A three-stage judgment is made for judgment objects with lower confidence.Based on the judgment of the original image,the second-stage judgment is carried out after the channel contrast is increased.Finally,sampling is performed on the region of interest where the second-stage confidence score wins for the third stage of judgment,and then judgment result is sent to the gated recurrent unit network for final inference.The result shows that through experiments on the same recyclables data set,the algorithm reduces the missed detection rate by 15.54%,and the false detection rate by 0.97%,while improves the accuracy rate by 16.51%.展开更多
基金the Humanities and Social Science Research Special Tasks of the Ministry of Education Funded Project(No.17JDGC008)。
文摘A D-G-YOLOV3 algorithm was proposed to identify and judge recyclables,which introduced a dense feature network to replace the feature pyramid network.The network closely connects and fits the feature maps and simulates human judgment mechanism.A three-stage judgment is made for judgment objects with lower confidence.Based on the judgment of the original image,the second-stage judgment is carried out after the channel contrast is increased.Finally,sampling is performed on the region of interest where the second-stage confidence score wins for the third stage of judgment,and then judgment result is sent to the gated recurrent unit network for final inference.The result shows that through experiments on the same recyclables data set,the algorithm reduces the missed detection rate by 15.54%,and the false detection rate by 0.97%,while improves the accuracy rate by 16.51%.
