Object Detection Using Computer Vision
Keywords:
Object Detection, Computer Vision, Deep LearningAbstract
Object detection has become an important technology in artificial intelligence and computer vision. Many industries now use this technology, including self-driving cars, medical image analysis, security systems, and manufacturing. This paper presents a detailed study of object detection methods through three main parts. The first part covers the basic theories and concepts of object detection. We explain the differences between image classification and object detection, and describe the main parts of detection systems. We also discuss how to measure performance using average precision (AP) and mean average precision (mAP). The section includes a review of how detection models have developed over time, from older methods to modern one-stage and two-stage approaches. We provide practical examples using Faster R-CNN. The second part focuses on the YOLO (You Only Look Once) algorithm, which represents single-stage detection methods. YOLO is popular because it works fast while keeping good accuracy. We explain how YOLO works, including how it divides images into grids and predicts bounding boxes and object types at the same time. We show detailed code examples for detecting objects in still images using YOLOv5. The last part extends to video object detection, which brings new challenges for real-time processing. We discuss various techniques to improve performance, such as model quantization, pruning, knowledge distillation, and combining object tracking with detection using DeepSORT. We demonstrate practical applications by showing how to detect and track cars in traffic videos. The demonstrations illustrate how pretrained object detection models, particularly Faster R-CNN and YOLOv5, can be applied to still images and video frames for instructional purposes. The article does not aim to provide a systematic benchmark comparison, but rather to present practical implementation examples for readers who are beginning to use object detection techniques.
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