Detection of traffic congestion in road-occupied electric power construction based on video recognition

Abstract

The detection of traffic congestion is now realized mostly by human monitoring and sensor monitoring. However， such detection devices are deficient in road-occupied electric power construction. To meet the needs of low equipment dependency and high accuracy of congestion detection in the road-occupied electric power construction， a detection method based on video data is proposed， which uses neural networks to extract features from video data and determine whether there is traffic congestion. In response to data deficiency in the road-occupied electric power construction， the generalization of the network is improved by making full use of the generic traffic scene dataset， and the adaptive learning method based on domain adversarial neural networks （DANN） is used to reduce the differential performance of two data domains in the feature extraction network. Semi-supervised learning （SSL） is proposed to reduce the manual labeling workload. The experimental results show that the proposed method can achieve an accuracy of 93.2% in traffic congestion detection and recognition in road-occupied electric power construction and has high application value.

Keywords

• road-occupied power construction
• congestion detection
• video recognition
• domain adaptation
• semi-supervised training