Detection for Dangerous Goods Vehicles in Expressway Service Station Based on Surveillance Videos

To improve the safety capabilities of expressway service stations, this study proposes a method for detecting dangerous goods vehicles based on surveillance videos. The information collection devices used in this method are the surveillance cameras that already exist in service stations, which allows for the automatic detection and position recognition of dangerous goods vehicles without changing the installation of the monitoring equipment. The process of this method is as follows. First, we draw an aerial view image of the service station to use as the background model. Then, we use inverse perspective mapping to process each surveillance video and stitch these videos with the background model to build an aerial view surveillance model of the service station. Next, we use a convolutional neural network to detect dangerous goods vehicles from the original images. Finally, we mark the detection result in the aerial view surveillance model and then use that model to monitor the service station in real time. Experiments show that our aerial view surveillance model can achieve the real-time detection of dangerous goods vehicles in the main areas of the service station, thereby effectively reducing the workload of the monitoring personnel.

Equipment used in agriculture

This standard applies to tractors, automobiles, agricultural, earthmoving and reclamation machines, technological equipment used in animal husbandry and feed production (hereinafter referred to as — machines) that are subject to long-term, short-term and inter-shift storage during non-working periods (in the absence of work, after or while waiting for repair), and also establishes general storage rules and a list of mandatory maintenance operations when storing machines, their components in farms, workshops, trading bases, repair enterprises, service stations and other agricultural enterprises.

Effectiveness of 3P Implementation (People, Process, Physical Evidence) And Company Performance At Different Levels Of Complexity And Divergence

The aim of this research is to know the differences of the level of complexity and divergence, the different effektiveness of 3P’s and the performance of two workshops (an authorized service station of motorcycle and an unauthorized one). The techniques used to collect data were: 1) interview, to know the general map of the company, 2) questionaires, to know data of complexity and divergence of the company, scores of perception concerning the effectiveness of 3P’s, 3) documentation, to know the daily total numbers of service within a year. The data analysing techniques used were ; the different percentage test use to know the different of level the complexity and divergence, the U Mann-Whitney Test used to know the different of 3P’s effectiveness and performances. The research found that : 1) there was a different of the level of complexity and divergence between an authorized service station of motorcycle and the unauthorized one. In the level of the different of complexity was 16,67%, the authorized service station has relatively higher level of complexity than unauthorized one. For this level of divergence is 10% and the authorized service station has relatively lower level of divergence than unauthorized one. 2) there was no different of 3P’s effectiveness between both service stations, at the variable people there was no diferent with z=-0,225 (p>0,05) and at the variable process there was no diferent with z=-0,122 (p>0,05) then at the variable physical evidence there was no diferent with z=-0,160 (p>0,05). 3) there was a different performance between an authorized service station and an unauthorized one. As specialy in giving minor service between the authorized service station and the unauthorized one namely z =-6,10 (p<0,05) and for the medium service, z = -6,786 (p<0,05) while for the major service,z = - 6,675 (p<0,05) and for the whole service between the authorized service station and unauthorized one the diferent was z = - 6,675 (p<0,05), but the authorized service station had much higher performance level than the unauthorized one in each service and doing full service.

Problems of Vehicle Identification by Tire Marks on the Soil

Scientific research is focused on the problems of vehicle identification by tire marks on the soil. It is argued that the car can either be a participant in a traffic accident or be used during theft of property or kidnapping. The constructional features of the wheels and tires of a car, which are important in the formation of tracks, are described. When the wheel comes into contact with the soil, the tread pattern is reflected. The features of the vehicle tire marks recorded by the investigator and the forensic scientist during the scene search have been determined. Photo or video recording of car wheel tracks is possible, as well as preparing the impression from the display of the tread on the soil. The possibilities of transport and traceological examination for studying the traces of car wheels have been established. The procedure for appointing an expert examination by an investigator is described. There is a list of materials that are provided for an expert study, as well as a list of questions that can be posed to an expert. It is argued that a problem in proving the involvement of a vehicle in the commission of a crime is the possibility of the vehicle tires or wheels to be replaced immediately after it has been committed. Recommendations regarding the identification of a vehicle based on tire marks on the soil are suggested. To establish the fact of changing tires on a car after the commission of a crime, the investigator must seize all sets of tires that are found in the suspect’s household or garage. It is possible to confirm the fact of changing tires on a particular car by interrogating employees of service stations located near the suspect’s residence. In addition to tire marks, to identify the vehicle, it is worth using the testimony of witnesses regarding the model and color of the car, traces of glass debris and paint chips at the accident site and damage on the car itself.

Layout Methods for Integrated Energy Supply Service Stations from the Perspective of Combination Optimization

Integrated energy supply service stations (IES) are a new type of transportation energy infrastructure offering the advantages of comprehensive functions and intensive land use while providing more convenient and efficient energy supply services. Through the analysis of service station characteristics, this study regards the IES as a spatially superimposed combination of various energy supply services, proposes a layout method from the perspective of combination optimization, and establishes a station optimization model for energy supply stations. This method aims to further coordinate and optimize the combination of various energy supply stations to achieve global optimization of the energy supply service system. Finally, this study uses a hypothetical situation for example analysis to verify the validity and rationality of the method. The layout plan proposed in this study has important theoretical and practical significance for how to achieve the optimal layout of an IES.

Research on Multi-Dimensional Optimal Location Selection of Maintenance Station Based on Big Data of Vehicle Trajectory

In order to rationally lay out the location of automobile maintenance service stations, a method of location selection of maintenance service stations based on vehicle trajectory big data is proposed. Taking the vehicle trajectory data as the demand points, the demand points are divided according to the region by using the idea of zoning, and the location of the second-level maintenance station is selected for each region. The second-level maintenance stations selected in the whole country are set as the demand points of the first-level maintenance stations. Considering the objectives of the two dimensions of cost and service level, the location model of the first-level maintenance stations under two-dimensional programming is established, and the improved particle swarm optimization algorithm and immune algorithm, respectively, are used to solve the problem. In this way, the first-level maintenance stations in each region are obtained. The example verification shows that the location selection results for the maintenance stations using the vehicle trajectory big data are reasonable and closer to the actual needs.