基于CS优化深度学习卷积神经网络的目标检测算法

目前对于形状比较复杂且密集摆放的工件,传统的工业机器人视觉分拣技术已经无法有效检测和识别。因此,为了提高生产线上分拣工件检测的准确率,提出了一种基于布谷鸟搜索算法(Cuckoo Search,CS)优化深度学习卷积神经网络(Convolutional Neural Network,CNN)的目标检测算法。首先对视觉分拣系统的组成进行了分析。然后采用经典的Faster R-CNN的模型结构来实现目标检测,并将CS优化算法应用到CNN模型的参数训练中,解决了反向传播的局部最优问题,同时提高了迭代速度。工件检测实验结果表明:相比于传统的CNN模型,提出CS-CNN模型具有更好的目标检测的准确率,提高了网络的收敛速度。

Target detection algorithm based on CS optimized deep learning convolutional neural network

At present, the traditional industrial robot visual sorting technology has been unable to effectively detect and identify workpiece with complicated shapes and dense placement. Therefore, in order to improve the accuracy of sorting workpiece detection on the production line, a target detection algorithm based on Cuckoo Search (CS) optimized deep learning Convolutional Neural Network (CNN) is proposed. The composition of the visual sorting system was first analyzed. Then the model structure of the classic Faster R-CNN is used to achieve the target detection, and the CS optimization algorithm is applied to the parameter training of the CNN model, which solves the local optimal problem of back propagation and improves the iteration speed. The experimental results of workpiece inspection show that compared with the traditional CNN model, the proposed CS-CNN model has better accuracy of target detection and improves the convergence speed of the network.