Abnormal object detection and recognition in the complex construction site via cloud computing

Chuang Wang (1) , Jiakun Li (2) , Tian Wang (2) , Peng Shi (3) , Hichem Snoussi (4) , Xin Su (5)

1 State Grid Beijing Electric Power Company
2 BUAA - Beihang University
3 Fujian Normal University [Fujian]
4 LM2S - Laboratoire Modélisation et Sûreté des Systèmes
5 Hohai University

For the construction site image understanding, object detection and recognition are the most important tasks. In the construction site with electrical equipment, the scene need to be monitored carefully to avoid accident. In our work, one anomaly detection method via the cloud computation is proposed. The method consists of the one-stage deep learning object detection model and the one-class classification. The one-stage object detection method detects and recognizes the objects in the scenes. Then, the one-class SVM alarms the abnormal region. The proposal algorithm has been tested on several scenes of real construction sites, and achieves fine results practicably.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Chuang Wang, Jiakun Li, Tian Wang, Peng Shi, Hichem Snoussi, et al.. Abnormal object detection and recognition in the complex construction site via cloud computing. Research in Adaptive and Convergent Systems (RACS'2019), Sep 2019, Chongqing, China. pp.71-75, ⟨10.1145/3338840.3355648⟩. ⟨hal-02380430⟩