Perception - 2D Object Detection

Here is a hands-on example for "Perception Problem-2D Object Detection Task", which uses YOLO to perform vehicle recognition on traffic scene clips. The download link for the complete project is:https://github.com/TOPSlearningcenter/2DD

Given belowModel Overview, click on the rightRun Code Online。

Model Overview

1. Model overview:

YOLO (You Only Look Once) is a mainstream two-dimensional target detection model. It was first published by Joseph Redmon and his team in the paper《You Only Look Once: Unified, Real-Time Object Detection》It was proposed in the book that it was continued to be optimized and developed by future generations, and it has reached the tenth generation so far. The basic idea of ​​this model is to transform the target detection problem into a regression problem, extract the feature vector of the image through a convolutional neural network, and simultaneously predict the bounding box coordinates and category labels in the image. YOLO performs global predictions across the entire image, enabling fast and real-time object detection. In the two-dimensional target detection task of motor vehicles and pedestrians, the input is the traffic flow image captured by the camera, and the output is the bounding box range and category of each detected target.

2. Model Architecture:

  • The YOLO architecture varies slightly across versions, but its core idea remains the same.
  • Backbone network: a series of convolutional layers extracts image features. Stacking layers from shallow to deep produces feature representations containing complex information.
  • Output prediction: after obtaining image feature vectors, the model predicts Object Detection bounding boxes and confidence scores, using fully connected layers after the convolutional layers.
  • Loss function: the loss has three parts corresponding to the outputs: bounding-box localization error, confidence error, and cross-entropy loss for class prediction.

3. Implementation:

  • Data preprocessing: split a video sequence into an ordered sequence of images at a chosen frame rate.
  • Model selection: choose and load a pretrained model according to the required inference speed and accuracy.
  • Visualization: run inference on images and render the predicted bounding boxes in a new image or video stream.