instal the new version for apple MathWorks MATLAB R2023a v9.14.0.2286388

MATLAB provides functions for preparing data for object detection, some of which are presented in this table: I will mainly highlight the MATLAB tools that allow you to streamline the visual inspection of PCBs and focus on the application, rather than spending too much time on data management and creating a deep learning network. This example shows how to detect, localize, and classify defects on PCBs using a YOLOv4 deep neural network. By detecting these defects, production lines can remove faulty PCBs and ensure that electronic devices are of high quality. Defects in PCBs can result in poor performance or product failures. PCBs contain individual electronic devices and their connections. Here, I will show you highlights from the documentation example Detect Defects on Printed Circuit Boards (PCBs) Using YOLO v4 Network. Visual inspection systems with high-resolution cameras efficiently detect microscale or even nanoscale defects that are difficult for human eyes to pick up. Visual inspection is the image-based inspection of parts where a camera scans the part under test for both failures and quality defects. Check out the updated example Detect Defects on Printed Circuit Boards Using YOLOX Network, which uses a YOLOX instead of a YOLO v4 object detector. Visual Inspection of PCBs Note: This section of the blog post describes an example that was updated in R2023b. In this blog post, I will show highlights from three new examples that apply deep learning: Feel free to take a deep dive into the machine learning release notes and deep learning release notes to explore all new features and examples.

There are many new examples in the documentation of the latest MATLAB release (R2023a) that show how to use and apply the newest machine learning and deep learning features.

Instal the new version for apple MathWorks MATLAB R2023a v9.14.0.2286388