Exploring Pathways in SIGMA-VANGUARD's Visual Intelligence

26 June, 2025

As we continue to build SIGMA-VANGUARD, our AI system for identifying and classifying military vehicles and aircraft from visual data, we’ve been deep in the exploration phase — investigating the most effective technical path forward. One of the key architectural decisions centers around how we approach object classification: should we rely on image embeddings and vector similarity, or pursue custom model training using object detection networks like YOLO?

The first approach we've explored involves extracting feature vectors from detected objects using a pretrained model (such as CLIP or a CNN backbone) and then comparing these vectors against a curated vector database of known vehicle and aircraft classes. This method is modular and fast to prototype. Once the object is localized in the image, we can map it into a semantic embedding space and find the closest match based on cosine similarity.

This vector-based technique offers a few key advantages. It’s flexible and data-efficient, especially valuable in domains like military recognition where labeled data is scarce. It also opens the door for zero-shot or few-shot classification, where new object types can be identified without retraining the model — only by updating the vector database. However, this method is inherently dependent on the quality of both the vector embeddings and the diversity of your reference samples. In visually complex or degraded images, it may struggle with false positives or category confusion between similar vehicle types.

In parallel, we’ve been experimenting with training a custom YOLOv11 model to directly detect and classify military vehicles from images. YOLO is a proven solution for real-time object detection, and its newer versions offer solid performance with fast inference times. With enough labeled data — especially bounding boxes and class tags — YOLO can learn to localize and classify trucks, tanks, IFVs, APCs, helicopters, and missile launchers with high precision.

The strength of the YOLO approach lies in its end-to-end integration: it detects and classifies in a single shot, which makes it highly efficient in production environments. It also tends to outperform feature-based matching in challenging conditions like cluttered backgrounds, occlusions, or unusual viewing angles. But YOLO’s main limitation is data hunger — it requires significant amounts of annotated training data, which can be hard to come by in niche domains like defense. Also, adapting the model to new categories involves retraining, which adds overhead.

Given these trade-offs, our current strategy is to continue parallel testing. For early prototyping and exploration, the vector-based approach allows us to iterate faster and evaluate scenarios with limited labeled data. As we collect more field imagery and refine our dataset, we plan to scale up YOLO training to improve localization accuracy and performance in operational settings.

Ultimately, the two approaches may coexist within SIGMA-VANGUARD. A hybrid system — where YOLO handles detection and embeddings are used for deeper classification or verification — could give us the best of both worlds. As the project evolves, we’ll share benchmarks, technical insights, and decision points with the broader community.