One post tagged with "ObjectDetection"

Vision AI has become a core technology in industry, security systems, and medical imaging. In many real-world applications, however, decisions are not based on a single image. Instead, multiple images are available: reference images, temporally shifted frames, multiple camera perspectives, or different imaging modalities.

Traditional Vision AI models have historically been designed to process individual RGB images. This assumption increasingly fails to reflect practical deployment scenarios.

Typical use cases that are only partially solvable with single-image approaches include:​

• Quality inspection using reference patterns
• Detection of small changes in largely static scenes
• Multi-camera object recognition
• Fusion of RGB and depth data
• Analysis of medical image series (e.g., MRI slice data)

In these scenarios, the decisive factor is not the isolated image content, but the comparison between images.

Multi-Image Vision AI as a Structural Advantage​

Multi-Image Vision AI refers to approaches in which multiple images are evaluated jointly. The ONE AI platform by ONE WARE natively supports this concept by integrating multiple image sources as equal inputs directly into the model architecture.

The key factor is not the number of images, but the model’s ability to structurally capture relationships, differences, and consistencies between them. In contrast, classical object detection models such as YOLOv8 process each image independently. Context from reference or comparison images is not available to the model.

In quality inspection, surveillance, and monitoring applications, the task is rarely to detect objects “freely” within an image. Instead, the goal is to reliably identify small deviations between a known reference state and a current image state.

Single-image models are forced to learn background, scene structure, and relevant objects simultaneously.

Reference-based object detection follows a fundamentally different paradigm. The reference image provides explicit contextual information. The AI no longer needs to learn what remains constant, but can focus directly on what has changed.

In the demonstrated ONE AI use case, reference and test images are processed in spatial alignment. In addition, a pixel-wise difference between both images is computed and used jointly with all color channels of both images as model input.

This overlap-difference representation leads to:

• Suppression of static image components (buildings, sky, background structures)
• Amplification of small, relevant objects or changes
• Reduction of the effective problem complexity for the model

Unlike single-image approaches, the comparison is not learned implicitly, but provided explicitly.

Benchmark Scenario: Drone and Bird Detection Using Reference Images​

To quantitatively evaluate this approach, a synthetic dataset was created consisting of paired images of an urban skyline scene (Figure 1). A reference image shows the scene without target objects, while the corresponding test image contains inserted small objects such as birds or drones.

Figure 1: Reference images (left) and corresponding test images with inserted target objects (right) for reference-based object detection