인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
In modern days, increasing weapon-related threats in public places have created an immediate need for intelligent surveillance systems to detect crime in real-time. Traditional surveillance systems have struggles with recognizing small objects, occlusion, and the time it takes to respond, which makes them ineffective in crowded and fast-changing situations. To overcome these challenges, the suggested system combines closed-circuit television (CCTV) surveillance cameras with advanced deep learning methods, image processing, and computer vision techniques for real-time crime prediction and prevention. This study proposes a hybrid deep learning framework that merges a Faster region convolutional neural network and Mask Region Convolutional Neural Network, named FMR-CNN. The novel approach FMR-CNN represents a significant advancement towards improving object recognition and segmentation of images and videos. It has been combined with YOLOv8 to increase the real-time detection speed and localization accuracy significantly. Such a combination enables the concurrent utilization of high-resolution spatial context information and rapid frame-wise predictions, thus making it well-suited for continuous video surveillance tasks. The model was trained and tested on a five labeled class annotated dataset, where MobileNetV3 features are extracted to simulate real-world surveillance conditions. Experimental results show the hybrid model attains detection accuracy of 98.7%, average precision (AP) of 90.1, and speed of 9.2 frames per second (FPS), and generalizes to varied lighting, occlusion, object scales, and reduced computational complexity, making it highly effective for crime prevention. Using these models benefits police departments and law enforcement agencies, as it allows them to detect criminal offenses earlier and avoid untoward situations.
#Computer science
#Convolutional neural network
#Artificial intelligence
#Deep learning
#Context (archaeology)
#Law enforcement
#Segmentation
#Object detection
#Computer vision
#Frame rate
#Crime prevention
#Frame (networking)
#Machine learning
#Closed circuit
#Pattern recognition (psychology)
#Telecommunications
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