Concerning the problems of large scale change and small sample dataset in rail tread block defects, a rail tread block defects detection method based on improved Faster Region-based Convolutional Neural Network (Faster R-CNN) was proposed. Firstly, based on the basic network structure of ResNet-101, a multi-scale Feature Pyramid Network (FPN) was constructed to achieve the fusion of deep and shallow feature information in order to improve the detection accuracy of small-scale defects. Secondly, the Generalized Intersection over Union (GIoU) loss was used to solve the problem of insensitivity to the position of the predicted border caused by regression loss SmoothL1 in Faster R-CNN. Finally, a method of Region Proposal Network by Guided Anchoring (GA-RPN) was proposed to solve the problem of the imbalance of positive and negative samples in the training of the detection network due to the large redundancy of anchor points generated by Region Proposal Network (RPN). During the training process, the RSSDs dataset was expanded based on image preprocessing methods such as flipping, cropping and adding noise to solve the problem of insufficient training samples of rail tread block defects. Experimental results show that the mean Average Precision (mAP) of the rail tread block defects detection based on the proposed improved method can reach 82.466%, which is increased by 13.201 percentage points compared with Faster R-CNN, so that the rail tread block defects can be detected accurately by the proposed method.

To solve the problem of personal location privacy leakage under the rapid development of the Internet of Things （IoT） technology, a location perturbation algorithm of Geo-indistinguishability based on the Region Of Interest (GROI) was proposed. Firstly, a random noise satisfying planar Laplacian distribution was added to the real location of the user. Secondly, the approximate location was obtained by the discretization operation. Thirdly, the query results were sanitized based on the given Region Of Interest (ROI), and the query errors were further reduced while the availability of the mechanism remained unchanged. Finally, experiments were carried out on Google map queries to compare the proposed algorithm with the geo-indistinguishable location privacy protection algorithm. The results show that the proposed algorithm has the average error of query results reduced at least 2% compared to geo-indistinguishable algorithm within a 6.0 km retrieval range, and the accuracy of query results better than that of geo-indistinguishable algorithm while the privacy level is not degraded. Especially for close-range retrieval, the proposed algorithm can reduce the query error.