Mechanical Identification of Extra Large Hydraulic Engineering Vehicles Based on Video Big Data
Shui-Qing Wu, Yu Wang, Zhong-Yuan Zhang · 2019
Traditional vehicle target detection algorithm requires selection of suitable features for different image and video scenes, which make poor generalization. This paper presents the mechanical identification of large-scale hydraulic engineering vehicle using SSD framework based on video big data. A large-scale hydraulic engineering vehicle uses image context information analysis to understand the detection target and predicts convolution characteristics of multiple scales. In the post-target detection process, we proposed a semantic window mining method to improve the recall rate, and we used a data-driven fuzzy object tracking method to realize accurate visual tracking of objects in video big data. The experimental results showed that the method has high recognition rate for hydraulic engineering vehicles, and has better accuracy and better effect than traditional target detection algorithms.