Think Different

 

  Introduction: In modern industrial production, ensuring the quality of products is paramount. Traditional methods of inspection involve human inspectors, but with advancements in machine learning and artificial intelligence, custom models are now capable of detecting defects and anomalies in images. This article explores the application of machine learning in visual inspection, discussing its benefits for manufacturers across various industries. It delves into how AI-based visual anomaly detection is becoming increasingly popular in the world of manufacturing. Traditional Inspection on Production Lines: Historically, manufacturers relied on human inspectors to monitor product quality. As production volumes increased, human inspection became inadequate, leading to challenges in maintaining consistent quality. Introduction of Automation for Anomaly Detection: Automation was a significant leap forward, allowing manufacturers to increase production volumes without compromising qualit...

                                                         Photo by Google DeepMind To improve model processing speed on a small microcontroller, you can consider the following strategies: 1. Optimize Your Model: - Use a model that is optimized for edge devices. Some frameworks like TensorFlow and PyTorch offer quantization techniques and smaller model architectures suitable for resource-constrained devices. - Prune your model to reduce its size by removing less important weights or neurons. 2. Accelerated Hardware: - Utilize hardware accelerators if your Raspberry Pi has them. For example, Raspberry Pi 4 and later versions have a VideoCore VI GPU, which can be used for certain AI workloads. - Consider using a Neural Compute Stick (NCS) or a Coral USB Accelerator, which can significantly speed up inferencing f...