ANALYSIS OF THE USEFULNESS OF VIDEO MONITORING RECORDS FOR THE PURPOSES OF IDENTIFYING THE PERPETRATORS OF HOMICIDES

The article presents content related to the assessment of the usefulness of video monitoring for the identification of perpetrators of homicides as well as qualifying it as an important element of crime prevention. It presents the impact of the growing tendency of mass use of public space monitoring systems on the increased sense of security and control over situations which may threaten society. Moreover, the issues related to a perpetrator’s awareness of the inevitability of preserving their image by means of visual monitoring and its impact on the manner of their conduct as well as the possible withdrawal from committing a prohibited act have been signalled. The article also attempts to explain the reasons for the long-term impunity of some killers from the 1960s to the 1980s in relation to the lack of certain technological solutions, and especially the lack of video surveillance in areas where it is commonly used today. The article also covers the current legal solutions allowing for the legitimate collection of images from video monitoring, including those related to the protection of personal data in connection with the processing of images of the perpetrator.

Vision-Based Melt Pool Monitoring for Wire-Arc Additive Manufacturing Using Deep Learning Method

Wire-arc additive manufacturing (WAAM) technology has been widely recognized as a promising alternative for fabricating large-scale components, due to its advantages of high deposition rate and high material utilization rate. However, some anomalies may occur during the deposition process, such as humping, spattering, and robot suspend. this study proposed to apply Deep Learning in the visual monitoring to diagnose different anomalies during WAAM process. The melt pool images of different anomalies were collected for training and validation by a visual monitoring system. The classification performance of several representative CNN architectures, including ResNet, EfficientNet, VGG-16 and GoogLeNet, were investigated and compared. The classification accuracy of 97.62%, 97.45%, 97.15% and 97.25% was achieved by each model. The results proved that the CNN models are effective in classifying different types of melt pool images of WAAM. Our study is applicable beyond WAAM and should benefit other additive manufacturing or arc welding techniques.

The Application of Visual Sensors in the Artistic Design of College Campus Landscape

The research purpose is to solve the problems of low efficiency, low accuracy, and high cost of traditional environmental landscape mapping and landscape volume measurement methods in the artistic design of college campus landscape and make up the defects that the traditional campus monitoring is vulnerable to adverse weather, which results in low monitoring accuracy. Primarily, a binocular stereo vision measurement based on Scale Invariant Feature Transform (SIFT) matching algorithm is proposed, which can realize accurate collection of environmental spatial information and measurement of landscape volume without contact in the process of campus landscape design. Then, the visual monitoring system of college landscape based on the Internet of Things (IoT) is constructed to realize real-time monitoring and early warning of human damage to campus landscape. The proposed method is verified by actual measurement of different objects and simulation experiments using simulation software. Ultimately, the application of visual sensors in artistic design of college campus landscape is analysed by literature analysis. The results show that (1) the error of the improved binocular stereo vision measurement designed here is 52.32% and 59.69% lower than that of the traditional measurement method when measuring the same object with different volumes and the volumes of different objects, respectively, which indicates that the measurement accuracy of the new method is higher. (2) The proposed landscape visual monitoring method based on IoT improves the image recognition accuracy by 21% compared with the traditional digital image monitoring method. The average recognition time is shortened by 12 ms, which ensures the accuracy and improves the recognition efficiency. (3) Through the analysis of existing literature, it is found that the binocular stereo vision sensor can be used to monitor the whole process of landscape construction in real time. The sensor can be combined with social networks, mobile terminals, and physiological monitoring equipment to comprehensively analyse and evaluate people’s preference for campus landscape. The proposed method has broad application prospects in campus landscape design, construction, and maintenance. The research purpose is to provide important technical support for the improvement of the overall image of the college campus and even the city for the design of landscape environment and the technical upgrading of maintenance work in the college campus.

Visual Monitoring as a Tool in Industrial Security Engineering. Case Study

The article presents the use of video monitoring in a production company on the example of municipal thermal power plant. After analyzing the hazards and work inconvenience occurring in the analyzed enterprise, video surveillance zones have been designated with the division into the indoor and outdoor monitoring system. Video surveillance is provided for production, auxiliary and delivery processes. Moreover, video monitoring performs a control function over the occupation health in workstations at risk of explosion or fire. In addition, observation points have been set up in the visual monitoring system, among which the internal ones are served by the dispatcher responsible for the production process, while the external are operated by security guards of the facility.