How does AI image processing combine with sensor fusion?

AI image processing combines with sensor fusion by integrating visual data from cameras with other sensor inputs (such as LiDAR, radar, ultrasonic sensors, or infrared cameras) to create a more comprehensive and accurate understanding of the environment. This fusion enhances decision-making in applications like autonomous vehicles, robotics, surveillance, and industrial automation.

How it works:

1. Data Collection: Cameras capture 2D/3D visual information, while other sensors provide complementary data (e.g., depth from LiDAR, speed from radar, or proximity from ultrasonic sensors).
2. Preprocessing: AI algorithms preprocess raw sensor data to filter noise, align formats, and extract features. For example, computer vision models detect objects in images, while point cloud processing refines LiDAR data.
3. Fusion: Sensor data is merged at different levels—raw data (low-level), features (mid-level), or decisions (high-level). AI models (like deep neural networks or Bayesian filters) analyze the combined inputs to resolve ambiguities or gaps in individual sensors.
4. Output: The fused output enables precise tasks such as object recognition, motion tracking, or obstacle avoidance.

Example:
In an autonomous car, cameras identify lane markings and traffic signs, while LiDAR measures distances to surrounding objects. AI fuses these inputs to determine the exact position of pedestrians or vehicles, even in low-light conditions where cameras alone may struggle.

Relevant Cloud Service (Tencent Cloud):
For scalable AI image processing and sensor fusion, Tencent Cloud offers TI-ONE (Intelligent Computing Platform) for training custom AI models and IoT Explorer to manage sensor data streams efficiently. These services support real-time analytics and model deployment for edge or cloud-based fusion systems.