Concerning the poor performance of the Single-Shot Refinement Neural Network for Object Detection (RefineDet) of the object detection network when detecting small sample classes in inter-class imbalanced datasets, a Subsection Weighted Loss (SWLoss) function was proposed. Firstly, the inverse of the number of samples from different classes in each training batch was used as the heuristic inter-class sample balance factor to weight the different classes in the classification loss, thus strengthening the concern on the small sample class learning. After that, a multi-task balancing factor was introduced to weight classification loss and regression loss to reduce the difference between the learning rates of two tasks. At last, experiments were conducted on Pascal VOC2007 dataset and dot-matrix character dataset with large differences in the number of target class samples. The results demonstrate that compared to the original RefineDet, the SWLoss-based RefineDet clearly improves the detection precision of small sample classes, and has the mean Average Precision (mAP) on the two datasets increased by 1.01 and 9.86 percentage points, respectively; and compared to the RefineDet based on loss balance function and weighted pairwise loss, the SWLoss-based RefineDet has the mAP on the two datasets increased by 0.68, 4.73 and 0.49, 1.48 percentage points, respectively.

Aiming at the ignorance of target structure in test topic distribution due to the lack of annotation in supervised Latent Dirichlet Allocation (sLDA) model, a sLDA fastener image classification model combined with global and local constraints (glc-LDA) was proposed. Firstly, the training images were manually labeled, and the training topic distribution with structural information was learned in sLDA model. Then, the test topic distribution was calculated to obtain the image category probabilities as global constraints, the topic similarities of training sub-blocks and test sub-blocks as local constraints. Finally, updated test topic distribution was obtained by weighted summation of training topic distribution with the product of global and local constraints as updated weights. The image category labels were obtained in Softmax classifier by the updated topics. The experimental results show that the proposed algorithm can express the structural information of fastener and compared with sLDA model, the distinction of each category of fastener images is enhanced, and the false detection rate is reduced by 55%.

Focusing on the ignorance of the image structure in Latent Dirichlet Allocation (LDA) model, a LDA fastener detection model integrated with image texture information, namely TS_LDA, was proposed. Firstly, a single-channel Local Binary Pattern (LBP) method was designed to acquire the image texture structure, and the texture information was treated as a label of visual word. The joint distribution of words and labels reflected the characteristics of an image structure. Secondly, those labels were embedded into LDA, and image topics were derived from words and labels. The improved distribution of topics considered the image structure. Finally, the classifier was trained and fastener states were identified on the basis of topic distribution. Compared with the LDA method, the differences between normal and disabled fasteners increased by 5%-35%, the average misdetection rate decreased by 1.8%-2.4% in the topic space of TS_LDA. The experimental results show that TL_LDA is able to enhance the accuracy of fastener image modeling, thus inspects fastener states more precisely.

Concerning the key problems of selecting threshold function in wavelet threshold denoising, in order to address the discontinuity of conventional threshold function and large deviation existing in the estimated wavelet coefficients, a continuous adaptive threshold function in the whole wavelet domain was proposed. It fully considered the characteristics of different sub-band coefficients in different scales, and set the energy of sub-band coefficients in different scales as threshold function's initial weights. Optimal weights were iteratively solved by using interval advanced-retreat method and golden section method, so as to adaptively improve approximation level between estimated and decomposed wavelet coefficients. The experimental results show that the proposed method can both efficiently reduce noise and simultaneously preserve the edges and details of image, also achieve higher Peak Signal-to-Noise Ratio (PSNR) under different noise standard deviations.

To ensure good dynamic performance, a dynamic simulation analysis is needed for Computer Numerical Control (CNC) floor type boring and milling machine in the design stage because of its complex structure. According to the request, a dynamic simulation idea with a practical effect was presented. The proposed scheme firstly obtained modal parameters of joint surface such as bed-slide, slide-column, column-spindle box via the modal test of CNC floor boring and milling machine tool prototype; then joint parameters obtained by finite element optimization identification of modal parameters and furthermore simulated by COMBIN14 element were used to establish the finite element model of CNC. With the dynamic simulation on the dyna971 software platform, a good dynamic characteristic was displayed due to reposeful waveform of stress and strain, while machine tool was subject to external force effect. At present, the research result had been integrated into batch production of this kind of machine.