Scene segmentation for Autonomous Vehicles and Drones

Semantic segmentation is a computer vision technique that involves classifying each pixel in an image into a predefined category, such as road, pedestrian, vehicle, sky, or building. Unlike traditional object detection that draws bounding boxes around objects, semantic segmentation provides a pixel-level understanding of the scene, which results in more precise localization and recognition of elements within the environment.

This detailed perception is crucial for autonomous vehicles and drones, as it enables them to navigate complex environments safely and efficiently. For example, an autonomous car needs to distinguish between the road, sidewalks, pedestrians, and other vehicles to make informed decisions like lane-keeping, obstacle avoidance, and pedestrian detection. Similarly, drones require accurate scene understanding for tasks such as safe landing, obstacle avoidance in flight, and environment mapping. Semantic segmentation ensures that these machines can interpret their surroundings with high precision, which is essential for reliable autonomy.

At NeuralSpike we prepared and trained segmentation models for autonomous vehicles. Our model detects classes such: road, sidewalk, and person. This allows AV to navigate through drivable, and safely avoid obstacles.
We deployed our model on an embedded device, which introduces several key challenges. First, the model must be lightweight and optimized to run efficiently on limited hardware resources, ensuring low latency and fast inference without compromising too much on accuracy. Since the system is battery-powered, energy efficiency is critical—every computation must be carefully managed to maximize battery life and prevent thermal issues. Additionally, the model must be highly reliable, as all processing is done locally on the device, without reliance on cloud computing. This local processing avoids latency issues and ensures consistent performance even when network connectivity is poor or unavailable, which is especially important for real-time applications like autonomous navigation where every millisecond counts.
To overcome the above challenges, we used our ml-toolbox, which allowed us to find optimal model architecture for the hardware. We also used knowledge distillation to improve model accuracy.
To enhance the robustness of our model, we leveraged our synthetic data generation framework to create additional training data. This approach was particularly valuable for generating edge cases—uncommon but critical scenarios such as sudden pedestrian crossings, extreme weather conditions, or unusual road obstacles. These events are difficult to capture in real life due to their rarity and the potential danger they pose to both drivers and pedestrians. By simulating these challenging situations, we were able to diversify the training dataset, helping the model learn to recognize and respond to a wider range of real-world conditions. This significantly improves the safety and generalization of our system, especially in high-stakes environments where even rare errors can have serious consequences.

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