This comprehensive tutorial walks you through YOLOv8 object detection—from environment setup and dataset preparation to model training, validation, testing, and conversion to ONNX and TensorRT—providing clear commands, code snippets, and visual results for each step.
The article proposes a two‑stage pipeline that first uses YOLOv8 to locate QR codes in high‑resolution images and then crops those regions for decoding with OpenCV’s WeChat QRCode, overcoming small‑code detection limits while maintaining speed and accuracy.
The 360LayoutAnalysis project from 360 AI Lab releases lightweight, yolov8‑based layout analysis models covering Chinese and English papers, Chinese research reports, and a general document scenario, providing fast inference, paragraph‑level detection, and open‑source code and weights for flexible document‑understanding pipelines.
This article details how to train a YOLOv8‑pose model for license‑plate detection, convert it to TensorRT engine, implement C++ inference and preprocessing, expose the functionality via CGO to Go, and assemble a lightweight web service for real‑time plate recognition.
This article demonstrates how to combine AI‑based object detection (YOLOv8) with robotic process automation (pyautogui) to automatically locate, drag and release slider captchas, covering data preparation, model training, screen capture, coordinate extraction, mouse simulation, and robustness improvements.
