“…Computer vision (e.g., convolutional neural networks (CNN), Object Detection algorithms (e.g., YOLO, SSD), Image Classification models (e.g., ResNet, Inception)), robotic systems (e.g., robotic arms, Gantry systems, Delta robotics, automated conveyor systems), and data analytics (e.g., machine learning, deep learning, artificial intelligence) as mentioned above, can assist in the identification, classification, and sorting of battery components and materials for efficient recovery and recycling. [ 253,254 ] For example, image analysis and computer vision tools, including OpenCV (Open Source Computer Vision Library), TensorFlow, PyTorch, MATLAB, Caffe, and scikit‐image as a Python library for image processing and analysis, can assist with battery recycling in pyrometallurgical, hydrometallurgical, and biological recycling methods.…”