When we use traditional computer vision Inspection technology to locate the vehicles,we find that the results were unsatisfactory,because of the existence of diversified scenes and uncertainty.So,we present a new meth...When we use traditional computer vision Inspection technology to locate the vehicles,we find that the results were unsatisfactory,because of the existence of diversified scenes and uncertainty.So,we present a new method based on improved SSD model.We adopt ResNet101 to enhance the feature extraction ability of algorithm model instead of the VGG16 used by the classic model.Meanwhile,the new method optimizes the loss function,such as the loss function of predicted offset,and makes the loss function drop more smoothly near zero points.In addition,the new method improves cross entropy loss function of category prediction,decreases the loss when the probability of positive prediction is high effectively,and increases the speed of training.In this paper,VOC2012 data set is used for experiment.The results show that this method improves average accuracy of detection and reduces the training time of the model.展开更多
基金supported in part by National Natural Science Fund of China (61806088, 61902160)Qing Lan Project of Jiangsu Province and Natural Science Foundation of Jiangsu Province (BK20160293)Changzhou Science and Technology Support Plan (CE20185044).
文摘When we use traditional computer vision Inspection technology to locate the vehicles,we find that the results were unsatisfactory,because of the existence of diversified scenes and uncertainty.So,we present a new method based on improved SSD model.We adopt ResNet101 to enhance the feature extraction ability of algorithm model instead of the VGG16 used by the classic model.Meanwhile,the new method optimizes the loss function,such as the loss function of predicted offset,and makes the loss function drop more smoothly near zero points.In addition,the new method improves cross entropy loss function of category prediction,decreases the loss when the probability of positive prediction is high effectively,and increases the speed of training.In this paper,VOC2012 data set is used for experiment.The results show that this method improves average accuracy of detection and reduces the training time of the model.
