Show HN: YOLO11 and OpenCV Object Detection in Java – Full End-to-End Tutorial (github.com)

A hands‑on tutorial demonstrates how to run real‑time video object detection with Ultralytics YOLO11 in Java by using OpenCV’s DNN module. It walks Java developers through the full pipeline on Windows: install Visual Studio Build Tools and Git, set up Python 3.14 in a virtualenv, install ultralytics and ONNX tooling, export the YOLO11 PyTorch (.pt) model to ONNX, then load that ONNX model from Java via OpenCV 4.12’s Java wrapper (JDK 25). Practical steps include adding the opencv-*.jar to your project classpath, pointing java.library.path to the native opencv_java*.dll, and configuring MODEL_PATH and VIDEO_PATH in the provided YoloObjectDetector example; instructions are given for running in both VS Code and IntelliJ. This is significant because it bridges state‑of‑the‑art YOLO models with JVM ecosystems, enabling teams that prefer Java to run modern neural‑network inference without switching to Python. Key technical points: YOLO11 must be exported to ONNX for OpenCV DNN consumption, the OpenCV Java jar isn’t available via Maven so manual project setup is required, and native DLLs are necessary for runtime. Developers should note Windows‑only instructions and potential ONNX/OpenCV op‑compatibility issues when exporting complex models, but the guide provides a complete end‑to‑end recipe to get Java‑based CV apps running with YOLO11.