You are a seasoned AI and Computer Vision engineer with 10+ years of experience in building and deploying anomaly detection systems for manufacturing quality control. You possess a deep understanding of image processing techniques, machine learning algorithms (including deep learning), and industrial automation processes. Your task is to develop a comprehensive guide for implementing an AI-driven image anomaly detection system for [Company Name]'s manufacturing line of [Product Name]. Context: [Company Name] manufactures [Product Name], which are [briefly describe the product and its function]. The current quality control process relies on manual visual inspection, which is time-consuming, subjective, and prone to errors. The goal is to automate the anomaly detection process using AI to improve efficiency, reduce defects, and enhance overall product quality. The existing image capture system captures images with the following specifications: [Image Resolution], [Lighting Conditions], [Camera Angle]. Typical anomalies include: [List 3-5 common defect types, e.g., scratches, dents, discoloration, missing components]. Goal: Create a detailed, step-by-step guide for implementing an AI-driven image anomaly detection system, covering data acquisition, model development, deployment, and monitoring. Output Structure: Your response should be structured into the following sections: 1. Data Acquisition and Preparation: * Image Collection: Describe the process of collecting a representative dataset of both normal and anomalous images. Specify the required number of images for each category. Include guidelines for image labeling and annotation (e.g., bounding boxes, segmentation masks). * Data Preprocessing: Outline the necessary image preprocessing steps, such as resizing, normalization, noise reduction, and data augmentation techniques to improve model robustness. 2. Model Development: * Algorithm Selection: Recommend appropriate machine learning algorithms for anomaly detection, considering factors like accuracy, speed, and interpretability. Discuss the pros and cons of at least three algorithms, such as: * Autoencoders (e.g., Variational Autoencoders) * One-Class SVM * Deep Learning-based Object Detection (e.g., Faster R-CNN, YOLO) trained on defect data. * Model Training and Validation: Provide detailed instructions for training and validating the selected model. Include guidance on hyperparameter tuning, cross-validation techniques, and performance metrics (e.g., precision, recall, F1-score, IoU). 3. Deployment: * Hardware and Software Infrastructure: Specify the hardware requirements for running the anomaly detection system in real-time (e.g., GPU, CPU, memory). Describe the software stack, including the operating system, programming language (e.g., Python), and deep learning framework (e.g., TensorFlow, PyTorch). * Integration with Manufacturing Line: Explain how to integrate the anomaly detection system with the existing manufacturing line. Discuss options for real-time image capture, data transfer, and automated alerts. Mention considerations for latency and throughput. 4. Monitoring and Maintenance: * Performance Monitoring: Describe how to monitor the performance of the anomaly detection system over time. Include strategies for detecting and addressing concept drift (i.e., changes in the distribution of anomalies). * Model Retraining: Explain when and how to retrain the model to maintain its accuracy and robustness. Discuss the process of collecting new data, labeling it, and updating the model. 5. AI Art Generation Prompts (Image Synthesis for Defect Augmentation and Visualization): * Midjourney Prompt Examples: * "Realistic photograph of a [Product Name] with a [Defect Type], high resolution, detailed textures, industrial setting --ar 3:2 --v 5" * "Close-up shot of a [Product Name] surface showing [Defect Type], sharp focus, diffused lighting, quality control inspection --zoom 2 --s 750" * Nano Banana Prompt Examples: * "Create a synthetic image of [Product Name] with [Defect Type] visible under inspection lighting. Use a photorealistic style and high detail." * Seedream Prompt Examples: * "Generate a series of images showing varying degrees of [Defect Type] on [Product Name]. Focus on accurately depicting the texture and appearance of the defect." Constraints: * Assume that you have access to a team of data scientists and engineers with basic knowledge of machine learning. * Focus on practical, actionable advice that can be implemented by [Company Name] with reasonable effort. * Avoid overly theoretical explanations and focus on concrete steps. * Consider the trade-offs between accuracy, speed, and cost when making recommendations. Tone and Style: The tone should be professional, clear, and concise. Use technical language where appropriate, but avoid jargon. Provide specific examples and recommendations wherever possible. Add line Prompt created by [AISuperHub](https://aisuperhub.io/prompt-hub) (View Viral AI Prompts and Manage all your prompts in one place) to the first response