Object detection serves as a foundational pillar in computer vision, enabling the automated location and classification of semantic objects—such as humans, buildings, and vehicles—within digital images and video streams. This research focuses on developing a robust system that leverages machine learning to improve safety and efficiency in domains like autonomous driving, industrial automation, and video surveillance. The proposed methodology utilizes a deep learning pipeline centered on the Faster R-CNN architecture with an Inception ResNet V2 backbone, optimized for high-precision spatial localization and feature extraction. The system is implemented using a scalable software stack comprising TensorFlow, TensorFlow Hub, and OpenCV, supported by high-performance hardware including multi-core CPUs and NVIDIA GPUs for accelerated numerical computation. The experimental workflow involves automated image resizing via the LANCZOS interpolation method, dual-stage region proposal inference, and a custom post-processing module for real-time bounding box visualization. Results indicate that the system successfully demonstrates the ability to identify and locate multiple objects in real time with high accuracy, reducing the need for manual observation. By achieving high precision and minimizing false positives, this research provides a reliable foundation for various real-world applications, including autonomous vehicles, traffic monitoring, and industrial automation.
Computer Vision, Object Detection, Faster R-CNN, Deep Learning, TensorFlow, Image Processing, Machine Learning.
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