Abstract: In construction scenarios,the management of incoming and outgoing vehicles involves problems such as the safety of construction site property and inaccurate recognition of contaminated licence plates.In response to these problems,a low-computing power vehicle entry/exit detection,recognition and tracking algorithm is proposed,which significantly reduces the human management cost and realises the intelligent management of vehicles at construction sites.Considering that the tasks of model identification and licence plate recognition at construction sites involve mixed detection of large and small targets,this paper proposes a cascaded multi-task end-to-end neural network framework.Firstly,the YOLO network is used to achieve the detection of vehicles at the construction site and the recognition of vehicle models,and on this basis,the vehicle licence plate localisation and recognition,and vehicle target tracking are achieved at the same time.Licence plate recognition is achieved with the help of lightweight neural network to achieve end-to-end accurate recognition algorithm; target tracking algorithm based on improved DeepSort improves the tracking of all vehicles in and out of the site.For the lack of construction site dataset,based on the existing gate monitoring data,a construction site vehicle detection dataset is constructed to train the multi-task cascade neural network,and the algorithm is further validated on the COCO2017 dataset,and the implementation results show the effectiveness and reliability of the algorithm.