Improved Roberts operator for detecting surface defects of heavy rails with superior precision and efficiency

An experimental platform accompanying with the improved Roberts algorithm has been developed to achieve accurate and real-time edge detection of surface defects on heavy rails.Detection results of scratching defects show that the improved Roberts operator can attain accurate positioning to defect contour and get complete edge information.Meanwhile,a decreasing amount of interference noises as well as more precise characteristic parameters of the extracted defects can also be confirmed for the improved algorithm.Furthermore,the BP neural network adopted for defects classification with the improved Roberts operator can obtain the target training precision with 98 iterative steps and time of 2s while that of traditional Roberts operator is 118 steps and 4s.Finally,an enhanced defects identification rate of 13.33%has also been confirmed after the Roberts operator is improved.The proposed detecting platform will be positive in producing high-quality heavy rails and guaranteeing the national transportation safety.

Mobile-Deep Based PCB Image Segmentation Algorithm Research

Aiming at the problems of inaccurate edge segmentation,the hole phenomenon of segmenting large-scale targets,and the slow segmentation speed of printed circuit boards(PCB)in the image segmentation process,a PCB image segmentation model Mobile-Deep based on DeepLabv3+semantic segmentation framework is proposed.Firstly,the DeepLabv3+feature extraction network is replaced by the lightweight model MobileNetv2,which effectively reduces the number of model parameters;secondly,for the problem of positive and negative sample imbalance,a new loss function is composed of Focal Loss combined with Dice Loss to solve the category imbalance and improve the model discriminative ability;in addition,a more efficient atrous spatial pyramid pooling(E-ASPP)module is proposed.In addition,a more efficient E-ASPP module is proposed,and the Roberts crossover operator is chosen to sharpen the image edges to improve the model accuracy;finally,the network structure is redesigned to further improve the model accuracy by drawing on the multi-scale feature fusion approach.The experimental results show that the proposed segmentation algorithm achieves an average intersection ratio of 93.45%,a precision of 94.87%,a recall of 93.65%,and a balance score of 93.64%on the PCB test set,which is more accurate than the common segmentation algorithms Hrnetv2,UNet,PSPNet,and PCBSegClassNet,and the segmentation speed is faster.