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首页> 外文期刊>Journal on Artificial Intelligence >Vehicle Target Detection Method Based on Improved SSD Model
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Vehicle Target Detection Method Based on Improved SSD Model

机译：基于改进的车辆目标检测方法SSD模型

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摘要

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.
机译：当我们使用传统的计算机视觉检查技术来定位车辆,我们发现结果是令人不满意的,因为多元化的场景和不确定性的存在。所以,我们提出一个基于改进的SSD的新方法模型。提取的算法模型,而不是能力使用的VGG16经典模型。新方法优化了损失函数,这样损失函数的预测补偿使附近的损失函数下降更为顺利零分。交叉熵损失函数的一类当预测,减少损失积极的预测概率高有效地,增加的速度训练。摘要VOC2012数据集用于实验。提高平均检测和精度降低了模型的训练时间。

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