摘要
A novel deep neural network compression model for airport object detection has been presented.This novel model aims at disadvantages of deep neural network,i.e.the complexity of the model and the great cost of calculation.According to the requirement of airport object detection,the model obtains temporal and spatial semantic rules from the uncompressed model.These spatial semantic rules are added to the model after parameter compression to assist the detection.The rules can improve the accuracy of the detection model in order to make up for the loss caused by parameter compression.The experiments show that the effect of the novel compression detection model is no worse than that of the uncompressed original model.Even some of the original model false detection can be eliminated through the prior knowledge.
提出了一种新的机场目标检测的深度神经网络压缩模型。该模型针对深层神经网络模型复杂、计算量大的缺点,根据机场目标检测的需要,从未压缩的模型中提取时空语义规则。通过将这些空间语义规则加入参数压缩后的模型中,以辅助检测。该规则可以提高检测模型的精度,弥补参数压缩带来的损失。实验表明,这种新的压缩检测模型压缩模型的效果并不比未压缩的原始模型差,甚至可以通过先验知识来消除一些原始模型的错误检测。
