An improved performance of RetinaNet model for hand-gun detection in custom dataset and real time surveillance video
Annotation
The prevalence of armed robberies has become a significant concern in today’s world, necessitating the development of effective detection systems. While various detection devices exist in the market, they do not possess the capability to automatically detect and alarm the presence of guns during robbery activities. In order to address this issue, a deep learning-based approach using gun detection using RetinaNet model is proposed. The objective is to accurately detect guns and subsequently alert either the police station or the bank owner. RetinaNet, the core of the system, comprises three main components: the Residual Neural Network (ResNet), the Feature Pyramid Network (FPN), and the Fully Convolutional Networks (FCN). These components work together to enable real-time detection of guns without the need for human intervention. Proposed implementation uses a custom robbery detection dataset that consists of gun, no-gun and robbery activity classes. By evaluating the performance of the proposed model on our custom dataset, it is evident that the ResNet50 backbone architecture yields outperforms for the accuracy in robbery detection that reached in 0.92 of Mean Average Precision (mAP). The model effectiveness lies in its ability to accurately identify the presence of guns during robbery activities.
Keywords
Постоянный URL
Articles in current issue
- Structural and spectral properties of YAG:Nd, YAG:Ce and YAG:Yb nanocrystalline powders synthesized via modified Pechini method
- Computational prediction in the problem of stereo image identification
- Comparison of application results of two speckle methods for study multi-cycle fatigue of structural steel
- Laser-induced thermal effect on the electrical characteristics of photosensitive PbSe films
- Homograph recognition algorithm based on Euclidean metric
- Solving the problem of preliminary partitioning of heterogeneous data into classes in conditions of limited volume
- Correction of single error bursts beyond the code correction capability using information sets
- A novel strategic trajectory-based protocol for enhancing efficiency in wireless sensor networks
- Automation of complex text CAPTCHA recognition using conditional generative adversarial networks
- Deep attention based Proto-oncogene prediction and Oncogene transition possibility detection using moments and position based amino acid features
- A method of storing vector data in compressed form using clustering
- Monocular depth estimation for 2D mapping of simulated environments
- Segmentation of muscle tissue in computed tomography images at the level of the L3 vertebra
- Providing operating modes for Coriolis vibration gyroscopes with low-Q resonators
- Collection and processing of environmental information in oil and gas production areas and solving other applied problems using active search methods (Review article)
- Using machine learning technologies to solve the problem of classifying infrasound background monitoring signals
- Study of the influence of the optical fiber output end shape on hydroacoustic processes in a liquid stimulated by microsecond pulses of Yb,Er:Glass laser radiation