article · European Journal of Applied Science Engineering and Technology
Crime prevention and detection are vital to national public safety, but traditional approaches relying on human intuition and limited data are reactive, tedious, and resource-intensive. This study investigates the shift from conventional practices, such as neighbourhood watch programmes, random stop-and-search operations, and foot patrols, as well as early technical methods like crime mapping and surveillance systems, towards artificial intelligence solutions. Drawing on a literature survey, local observation, and global news, the investigation highlights how machine learning algorithms can analyse extensive datasets to forecast criminal activity and reshape law enforcement operations. Furthermore, computer vision models can evaluate visual feeds from surveillance cameras to identify and respond to criminal events. The review recommends that law enforcement agencies integrate artificial intelligence to modernise security, whilst calling for further research and the creation of deep learning frameworks dedicated to crime detection.
Traditional law enforcement approaches often consume significant time and resources while responding only after crimes occur. Transitioning to artificial intelligence tools, such as predictive algorithms and automated visual monitoring, allows agencies to anticipate offences and process surveillance data more effectively, potentially improving community safety and operational efficiency.
The research outlines potential applications in predictive policing software and automated surveillance analysis using machine learning and computer vision. The targeted users are law enforcement agencies seeking to modernise crime detection operations. Because the work is an exploratory literature survey recommending the future design of deep learning frameworks, the concepts appear to be at an early conceptual stage rather than ready for immediate deployment.
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Crime prevention and detection are critical components of public safety in any nation. Traditionally, crime prevention and detection approaches relied on human intuition and limited data, resulting in reactive and resource-intensive methods. However, recent advancements in artificial intelligence (AI) offer a paradigm shift, enabling proactive, data-driven approaches. This study explores the evolution from conventional crime prevention and detection methods to cutting-edge AI solutions. It employs a literature survey, local observation, and global news approach to examine the current state of the art in AI-driven approaches. Traditional crime prevention methods, such as neighbourhood watch programs, random stop-and-search initiatives, and foot patrols, are examined alongside technological approaches, such as surveillance systems, crime mapping, and geographical profiling. These conventional techniques are tedious and time-consuming leading to inefficiency. Findings from the study revealed that AI has the potential to revolutionize crime prevention and detection through its subfields, such as machine learning and computer vision. Machine learning algorithms can process large amounts of data to forecast potential criminal activity, thus transforming law enforcement operations. Also, computer vision models can utilise visual data from surveillance cameras and other sources to analyse, identify, and respond to crimes. The study recommends the integration of AI into law enforcement agencies for crime prevention and detection to transform societal security. In addition, it emphasizes the need for further research in this domain. The study also recommends the development of an efficient framework and model for crime detection based on deep learning to enhance public safety.
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DOI: 10.59324/ejaset.2024.2(2).20
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