KEYWORDS: Data conversion, Data centers, Detection and tracking algorithms, Prisms, Target detection, Reliability, Precision measurement, Optimization (mathematics), Data processing, Data modeling
In the precision engineering measurement, the total station is often used for free station measurement, and improving the accuracy of stitching the measurement data of each station is the premise of widely used total station measurement by free setting. This paper is based on the quadratic method to study the stitching method of data measured by total station with free set-up stations. The total station data is obtained by free setting based on the quaternion method, and the method of stitching the data is investigated. Taking the deformation monitoring of the wooden tower in Yingxian County as a case study, a total station was measured by free setting, and the quadratic algorithm was used to realize the coordinate conversion between stations by point name matching, which is comparable to the stitching results of Leica's Cyclone software. In this paper, the improved quaternion algorithm is applied to the free station splicing of total station with the features of fast matching, coarse difference detection and rejection, optimization of redundant observation of the same name point splicing algorithm, inclusion of splicing error correction, and high splicing accuracy.
An enhanced YOLOv4 multi-spectral fusion pedestrian detection approach is proposed to address the problem of fusion network robustness in pedestrian detection. This method can effectively and accurately complete pedestrian detection. First, the feature extraction backbone is upgraded, and channel attention Module CAM and spatial attention Module SAM are added to the feature extraction backbone to allow for adaptive feature layer adjustment. Fusion processing based on channel and pixel direction is performed on the altered feature layer, and then the fusion layer is anticipated. The experiment is performed on the KAIST Dataset, and the capacity to generalize was assessed using the OTCBVS Benchmark Dataset. The proposed multi-spectral fusion detection approach is effective, according to the experimental results. The log-average miss rate (MR) reaches 11.03 and 8.79 throughout the full day and night when the false positive per image (FPPI) is 10-2~100 , and it also achieves good detection performance during the day. The proposed multi-spectral fusion detection approach is universal in various data sets, according to the generalization ability analysis experiment. Pedestrian detection accuracy may be accomplished adaptively regardless of whether it is daytime or nighttime detection, and speed is substantially enhanced.
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