Research on bolt image detection method based on a fusion of CNN and SVDD
In the traditional fastener quality inspection industry,workers are responsible for quality assessment of whether the product is qualified or not.However,the low efficiency of manual inspection,easy fatigue,and high false detection rate have become critical problems restricting the intelligence of the fastener industry.This paper proposes a bolt anomaly detection model with the fusion of convolutional neural network(CNN)and support vector data description(SVDD)to address this problem.First,the image acquisition device captures the full-surface image information of the bolt by setting multiple cameras in all directions,and the image is input to the convolutional neural network to extract the bolt image features layer by layer to obtain the middle and high-level features of the bolt;then,SVDD is used as an anomaly detection classifier,and for the problem of sample imbalance caused by the shortage of online acquisition of bolt defect samples,a convolutional self-encoder is proposed to establish a pre-training process,and the learned weights are transferred to the deep SVDD model as the initial weights.The experimental results show that compared with other bolt detection algorithms,the proposed fusion model can achieve better recognition results on the self-constructed bolt side image set,head image set,and bottom image bolt set.The space complexity is controlled within a specific range which has good application value and market promotion prospects.
NETL
NSTL
万方数据
维普
