Image Recognition Application for Weapon Detection

In public safety and security, the application of artificial intelligence (AI) in image recognition for weapon detection has become a critical component for identifying potential threats in real-time. This technology enhances the capabilities of surveillance systems and law enforcement agencies in maintaining public safety.

Use Case
Object Recognition

Advanced Surveillance System Enhancement for Security :  The client regularly deals with security monitoring across various infrastructural facilities. The process of manually searching through video footage to identify potential threats is not only exhausting but also time-consuming. Therefore, the client needed an efficient solution to automate these processes wherever possible and eliminate the need for manual review.

To address the client's issue, our team delved into the potential of machine learning algorithms (ML) and the available surveillance data to detect possible security threats within the footage, aiming to provide a ranked list of potential risks.

Our engineers decided to leverage computer vision algorithms based on embedding learning to tackle this task.


 Intelligent Computer Vision for Security Threat Detection : Our team, skilled in computer vision and image analysis software development, began with a comprehensive study of the client's needs, analysing use cases, exploring existing solutions, and importantly, understanding the sources of surveillance data available. This research was the initial phase in our three-phased approach – investigation, model development, and experimental part for idea validation.

We commenced by constructing and evaluating a neural network model that could predict encoded vectors for surveillance images, allowing us to measure the similarities and differences between various segments of footage.

Working with unlabeled data from the client and open-source databases, our team developed methods to extract meaningful insights from the surveillance images to be used in the solution. We achieved success by addressing indirect and implicit tasks of visual classification and adapting these to learn the subtle nuances of potential security threats.

The final step involved a series of experiments with both high- and low-level graphic processing, including Optical Character Recognition (OCR), to enhance the model's ability to identify threats accurately. The amalgamation of the most effective techniques formed the solution to the client's problem.


Streamlined Security Operations with AI-Powered Surveillance : Our company has provided the client with an innovative solution to automate the detection of potential security threats within surveillance footage.

The client has reaped the following benefits from our collaboration:

     1. Automated Threat Detection: Our solution scans surveillance footage in real-time, identifying potential threats without human intervention.

     2. Streamlined Security Processes: The system's ability to quickly identify and rank potential threats has expedited the client's security operations.

     3. Reduced Workload: With the automation of threat detection, the client's employees can focus on responding to threats rather than identifying them, significantly      reducing their workload.

Techstacks Used

Technologies and Tools
Machine Learning (ML): TensorFlow, PyTorch, AutoML Computer Vision (CV): OpenCV, TensorFlow Object Detection API Deep Learning: TensorFlow, PyTorch, Keras Infrastructure: AWS, Azure, GCP, NVIDIA CUDA, Docker, Kubernetes Data Storage: AWS S3, Azure Blob Storage, PostgreSQL, NoSQL databases Data Ingestion: IoT Devices, UAVs/Drones, API Integrations Development: Python, C++, Jupyter Notebooks, Git CI/CD: Jenkins, GitLab CI Monitoring: Grafana, Kibana, Prometheus, PagerDuty, ELK Stack Deployment: TensorFlow Serving, ONNX, Terraform, Ansible

Get Custom Solution, Estimates  &
Recommendations with Confidentiality!

Let’s spark the Idea

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.