Real-time Custom Object Detection System Using Transfer Learning

Abstract

In the last several years, every sector has been affected by deep learning, which gained a positive impact on achieving the new top in traffic control, people record counting, and public security becoming more advanced and powerful. Due to such constraints in the security sector, there needs to be more computing methodologies in development. Security and Surveillance sectors are overgrowing due to various deep learning algorithms, which are helping to capture and record live video streams to detect objects or human facial recognition with maximum possibilities and high precision. Some convolutions do not provide GPU acceleration over their convolution model parameter. Which scales down the ability of the engine. In the end that leads to generating quality issues in practical applications. Here, we extend the limit of the data-driven tasks by providing real-time solutions to solve the specific object detection problem and propose a robust & handy real-time object detection solution with scalable actions with more precision over the previous bottlenecks.