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Car Object Detection

Car object detection is the process of identifying and locating cars in an image or video stream using computer vision algorithms and machine learning techniques. This task is usually performed using deep learning models such as convolutional neural networks (CNN) and object detection frameworks such as YOLO (You Only Look Once) or SSD (Single Shot Detector) . The
automotive object detection has many applications in areas such as autonomous vehicles, traffic monitoring, and security monitoring.

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Car Detection

Vehicle tracking rings are used to track vehicles passing through a specific area; for our approach, the traffic lights.

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How to Detect Car Objects in a Frame?

The car object detection process includes the following steps:

  1. Image Acquisition: Acquiring a digital image or video image containing one or more vehicles.
  2. Preprocessing: Image preprocessing to improve functionality and reduce noise. This step may involve resizing the image, applying filters, and normalizing.
  3. Feature Extraction: The image goes through a pre-trained CNN to extract high-level features from the image. These characteristics are then fed into an object detection algorithm.
  4. Object detection: This step involves predicting the probability that each bounding box contains a car and refining the bounding boxes using nonmaximal suppression.
  5. Post-processing: The final step is to filter detected cars based on their size, orientation, and location to eliminate false positives.
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