基于混合结构卷积神经网络的目标快速检测算法

Target Fast Detection Algorithm Based on Hybrid Structure Convolutional Neural Network

为提高基于卷积神经网络(CNN)目标检测算法的检测速度,提出一种基于混合结构CNN的目标快速检测算法。采用基于CNN的Faster R-CNN目标检测框架,对其CNN进行优化。基于多层感知器结构,提出CR-mlpconv卷积层结构。在网络浅层采用C. Re LU策略,同时结合CR-mlpconv层结构和C. Re LU策略,合理设计层参数,构成卷积神经网络。将该卷积神经网络融合到Faster R-CNN检测框架中,实现目标快速检测。实验结果表明,在检测精度的适当影响范围内,该算法能够减少网络模型参数并降低网络模型的内存消耗,提高网络的实时性。

In order to improve the detection speed of the Convolutional Neural Network(CNN)target detection algorithm,a target fast detection algorithm based on hybrid structure CNN is proposed.CNN is optimized by CNN-based Faster R-CNN target detection framework.Based on the multilayer perceptron structure,a CR-mlpconv convolutional layer structure is proposed.The C.ReLU strategy is adopted in the shallow layer of the network,and the CR-mlpconv layer structure and the C.ReLU strategy are combined to design the layer parameters reasonably to form CNN.CNN is merged into the Faster R-CNN detection framework to achieve rapid target detection.Experimental results show that compared with the Faster R-CNN+ZFnet algorithm,the algorithm can reduce the network model parameters,reduce the memory consumption of the network model,and improve the real-time performance of the network.