YOLOv5

YOLOv5 ("You Only Look Once" version 5) is an open-source object detection framework developed by Ultralytics and released in 2020. Built on a CSPDarknet53 backbone with a PANet neck and anchor-based detection head, it processes entire images in a single neural network pass, achieving real-time inference speeds while maintaining high accuracy. It exports to TensorRT, ONNX, TFLite, and CoreML, making it trivially portable onto embedded compute modules including the Nvidia Jetson family. CSIS analysis published in April 2026 confirmed that Russia's V2U autonomous loitering munition uses YOLOv5 as its target-recognition layer, running on a Jetson Orin module.

YOLOv5's architecture is designed for speed: on an Nvidia A100 via TensorRT, the baseline model achieves 1.06 ms inference latency, while Jetson-optimised deployments run at usable speeds for real-time scene processing. The GitHub repository has accumulated over 50,000 STARs and remains one of the most widely forked computer vision projects in existence. Its open licence and straightforward fine-tuning pipeline have made it the default starting point for custom detection tasks, including military targeting research.

The V2U deployment illustrates a broader pattern in the Ukraine conflict: publicly available open-source models, fine-tuned on battlefield imagery for target classes such as armoured vehicles and personnel, provide a rapid path to autonomous engagement without the cost or lead time of bespoke military AI. YOLOv5's combination of edge-AI compatibility, documented fine-tuning ease, and publicly available pre-trained weights makes it a recurring component in autonomous drone programmes on both sides of the conflict.