Research On Identification Algorithm Of Personnel Abnormal Behavior In Oil Pipeline Scenario

In recent years,in order to solve the problem of uneven distribution of oil resources in China,the country has been vigorously developing the oil pipeline transportation industry,while the safety of oil pipelines has become increasingly serious,in which vandalism is the main hazard affecting the safe operation of oil pipelines,which not only causes huge economic losses,but also has a high risk of causing oil leaks,leading to serious damage to the surrounding ecological environment,and even triggering explosive accidents..Therefore,it is imperative to monitor the abnormal behaviour of people in the pipeline transportation scenario.The paper uses convolutional neural network technology in deep learning to research and improve the recognition algorithm of abnormal behaviour of personnel,so as to achieve the recognition of abnormal behaviour of personnel in oil pipeline transportation scenarios and assist management personnel in ensuring the safety of oil pipeline transportation.The main contents and innovations include:1.Due to the lack of public datasets applicable to oil pipeline scenarios,in order to solve the problem of acquiring model training data,we filmed behavioural videos in simulated experimental scenarios by ourselves,and selected some applicable behavioural videos from several large behavioural datasets such as UCF101,Kinetics700,HDBM51,etc.,and fused them to build a personnel abnormal behaviour dataset for oil pipeline scenarios.2.The paper builds five deep learning-based algorithmic models for identifying anomalous behaviours of people in oil pipeline scenes,i.e.using C3D,VGG,ResNet3D,DenseNet3D and Inception3D as backbone networks to extract video data features respectively,and designs comparative experiments to judge the performance of each model.The experimental results show that the Inception3D network has better learning ability and can more adequately extract RGB image features from video streams with abnormal behaviours of people.3.In this paper,a novel dual-stream convolutional neural network model is proposed.With the feature that the spatial information network algorithm can fully acquire the image spatial features with only simple pre-processing,the static features of abnormal behaviour in the video stream are extracted using the Inception3D network.At the same time,the ResNet3D network is used to extract dynamic features of the recognition target with the help of the temporal information network,which can extract motion information between consecutive frames of the target.And the training method of migration learning is used to improve the two networks respectively,and finally the recognition results of the two networks are averaged and fused to achieve the classification and recognition of specific behaviours.Through multiple sets of comparison experiments,it is proved that the network model proposed in this paper can better distinguish a variety of abnormal behaviours and effectively improve the recognition ability of abnormal behaviours.The recognition accuracy in the constructed dataset of abnormal behaviours of personnel facing oil pipelines reaches 95.38%,and the detection speed of video streams reaches 30FPS after accelerated by two NVIDIA RTX3090 GPUs,which meets the real-time recognition requirements of video monitoring,realizes the recognition of abnormal behavior of personnel in oil pipeline scenarios,and can effectively assist in solving the difficult problem of oil pipeline safety assurance.