AI Object Detector (Frontend)
It can detect and name the objects which are shown in the camra feed.
Intro
In this project, I developed an AI-based object detector using the COCO SSD (Single Shot MultiBox Detector) model. The goal was to create a robust system capable of identifying and classifying objects in real-time from images and video streams. This object detector can be used in various applications, such as surveillance, autonomous driving, and interactive systems.
Problem
Real-time Processing: Achieving real-time performance without compromising detection quality.
Integration: Integrating the object detector into different applications and platforms.
Approach
Real-time Processing: Leveraged hardware acceleration (e.g., GPUs) and optimized code to ensure the object detector operates in real-time.
Integration: Developed a flexible API to integrate the object detector into various applications. The model was also tested on different platforms to ensure compatibility.
Result
High Accuracy: Successfully detected and classified multiple objects in diverse environments.
Real-time Performance: Operated at a frame rate suitable for real-time applications.
Conclusion
The AI object detector project demonstrated the potential of COCO SSD in developing efficient and accurate object detection systems. The solutions to the challenges faced during the development process ensured the model's robustness and versatility.
