张科,吴嘉琪,陈威成,闫云凤,齐冬莲
ZHANG Ke,WU Jiaqi,CHEN Weicheng,YAN Yunfeng,QI Donglian

• 1:广东电网有限责任公司广州供电局
• 2:浙江大学电气工程学院

摘要(Abstract)：

目前对道路交通拥堵的检测多采用人为监控或传感器监测的方法，而电力占道施工场景往往缺乏此类检测设备。为满足该场景下拥堵检测的低设备依赖性和高准确性的需求，提出一种利用视频数据的检测方法，采用神经网络对视频数据进行特征提取并判断是否造成交通拥堵。为了解决电力占道施工场景下数据较少的问题，利用通用交通场景数据集提升网络的泛化性，采用域对抗迁移网络自适应学习方法弱化两种数据域在特征提取网络中的差异性表现。为了减少人工标注工作量，提出一种半监督学习方式。实验结果表明，所提方法在电力占道施工场景道路拥堵检测识别任务中达到了93.2%的准确率，具有较高的应用价值。
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.

关键词(KeyWords)： 电力占道施工;拥堵检测;视频识别;域适应学习;半监督训练
road-occupied power construction;congestion detection;video recognition;domain adaptation;semisupervised training

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目（U1909201）;; 浙江省自然科学基金探索项目（Q21F030038）;; 浙江省科技“尖兵计划”项目（2022C01056）

作者(Author): 张科,吴嘉琪,陈威成,闫云凤,齐冬莲
ZHANG Ke,WU Jiaqi,CHEN Weicheng,YAN Yunfeng,QI Donglian

DOI: 10.19585/j.zjdl.202305012

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