Modern vehicles recognize traffic signs, pedestrians, or other cars not just through simple sensors anymore. They analyze visual data in real-time to understand their environment and respond accordingly. This is made possible by so-called Convolutional Neural Networks (CNNs). But how do they actually work and what challenges lead to the use of these neural networks?

Computer Vision: Image Recognition & Features

Let’s take a simple example: We want to perform image classification and find out which object is visible in this image. Unlike object detection, image classification does not concern us with the position of the object; rather, it is about assigning a specific class to the visible objects based on predefined characteristics.

The example image has a pixel density of 1000 x 1000 pixels. That means: 1,000,000 individual pixels.

In the field of Computer Vision, each pixel is a so-called feature, meaning a variable that is used by the AI for the respective task (e.g., image classification). 1,000,000 features is already enormous, and with colored images, it gets even larger: With three color channels (red, green, blue), we end up with 3,000,000 pixel values.

This high number of features has consequences:

1. Higher computational costs  → More features mean more complex networks & more trainable parameters.

2. Higher storage costs → More features mean a higher dimension and a greater number of required training data (the Curse of Dimensionality).

3. Higher personnel costs → More training data must be labeled, increasing the personnel effort.

To reduce these costs, the number of features must be decreased without losing essential image information. This is exactly where Convolutional Neural Networks demonstrate their strengths.

What are CNNs?

Convolutional Neural Networks (CNN) are a specialized form of artificial neural networks that were developed particularly for image and video processing. They analyze local image areas using filters (so-called kernels). These filters “slide” across the image and recognize patterns such as edges, shapes, or textures regardless of where they appear in the image.

 A typical CNN structure consists of:

• Convolutional Layers recognize local patterns (e.g., horizontal/vertical edges, color gradients)

• Pooling Layers reduce the amount of data by summarizing neighboring pixel values without losing relevant information

• Fully Connected Layers combine the extracted features to make a classification or decision

• Depending on the task, e.g., a Softmax function that outputs a probability for each class

 The advantages of this network are clear:

1. Fewer parameters due to local filters and parameter sharing → more efficient calculations

2. Automatic feature extraction instead of manual feature definition

3. Translation invariance  → an object is recognized even when it shifts in the image

4. Better generalization due to reduced complexity

Thanks to these properties, CNNs are now the foundation of modern driver assistance systems – from traffic sign recognition to the analysis of complex traffic scenarios.

If you are interested in topics related to autonomous systems and object recognition, then follow our upcoming tech contributions. Among other things, we will take a detailed look at Convolutional Neural Networks – and many other exciting topics.