REAL-TIME OBJECT DETECTION USING YOLOV7

Abstract

This project presents the implementation and evaluation of a real-time object detection system using the YOLOv7(You Only Look Once, version 7) architecture, one of the most advanced and efficient models in the YOLO family.YOLOv7 introduces several improvements over previous versions, offering faster inference speed and higherdetection accuracy, making it suitable for deployment in real-world applications such as surveillance, autonomousnavigation, and industrial automation.The project is developed in Python and leverages the PyTorch deep learning framework. It integrates the YOLOv7architecture to perform object detection on images and video streams with minimal latency. The system architectureincludes a robust backbone for feature extraction, a feature aggregation neck, and a detection head responsible forpredicting object classes and bounding box coordinates. The model has been trained on large-scale datasets andsupports transfer learning, enabling users to fine-tune the network for custom object detection tasks.The implementation includes pre-trained weights for immediate deployment as well as utilities for dataset preparation,model training, and inference. Real-time detection is facilitated through optimized data pipelines and GPUacceleration, making the system efficient and scalable. The interface allows users to process static images, webcamfeeds, or video files, returning annotated outputs with confidence scores and class labels.Additionally, the project supports customization, allowing users to define new object classes, annotate data, and retrainthe model. The modular structure ensures flexibility and ease of integration into larger computer vision pipelines orIoT systems. Through experimental evaluations, the YOLOv7 model demonstrates high performance in terms ofprecision, recall, and speed across a variety of use cases. This makes it an ideal choice for applications requiringreliable object recognition in real-time environments.This project not only demonstrates the capabilities of modern object detection algorithms but also serves as a practicalframework for developers and researchers looking to build scalable and accurate vision-based systems using state-ofthe-art machine learning techniques.