{"id":10086,"date":"2023-06-25T12:28:02","date_gmt":"2023-06-25T16:28:02","guid":{"rendered":"https:\/\/dronebotworkshop.com\/?p=10086"},"modified":"2023-06-25T12:28:02","modified_gmt":"2023-06-25T16:28:02","slug":"esp32-object-detect","status":"publish","type":"post","link":"https:\/\/dronebotworkshop.com\/esp32-object-detect\/","title":{"rendered":"ESP32-CAM Object Detection with Edge Impulse"},"content":{"rendered":"\n

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If you\u2019re after a simple and inexpensive Object Detection system, consider using an ESP32-CAM. This 9-dollar camera board is probably already in your parts’ drawer, and with the right training, it can be used for simple Object Detection.<\/span><\/p>\n

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Today we\u2019ll see how to use Edge Impulse to train an ESP32-CAM board to detect objects.<\/span><\/p>\n

Introduction<\/span><\/h2>\n

When trying to keep pace with the dizzying speed of technology, one thing that never ceases to amaze me is how a cutting-edge application can go from costing millions of dollars to costing pennies, all in the space of a few years.<\/span><\/p>\n

One such technology is Object Detection, the ability of a machine to recognize an object (or several objects) using a video camera.\u00a0 Only a few years ago, \u201cplaying\u201d with applications like this demanded huge computers, often with big (and expensive) graphics cards.\u00a0<\/span><\/p>\n

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We have also used SBCs like the NVIDIA Jetson Nano, which is essentially an AI-dedicated GPU, for these tasks. This sort of hardware is required to run the neural networks needed for machine learning.<\/span><\/p>\n

But now we have a new generation of microcontrollers that can run a small version of machine language, something called Embedded ML or Tiny ML. We\u2019ve already seen how well the Kendryte K210 performs in the DFRobot Husky Lens using this technique.<\/span><\/p>\n

Today we\u2019ll see that even the ESP32 is capable of simple object detection. If your needs are simple, you may be able to fulfill them with an inexpensive ESP32-CAM board.<\/span><\/p>\n

Edge Computing<\/span><\/h3>\n

With the reduction in size that comes with microcontrollers also comes a reduction in price, so it\u2019s now possible to use object detection as part of an \u201c<\/span>edge computing<\/span><\/i>\u201d application.<\/span><\/p>\n

Edge computing<\/span><\/i> is a model of computing that involves processing data closer to its source or at the “edge” of the network, rather than relying on a central location like a main computer or cloud-based service.<\/span><\/p>\n

By using low-cost cameras and microcontrollers, we can implement a system that can recognize specific objects and report their position back to a central controller or react directly to their presence.<\/span><\/p>\n

ESP32 vs. Other Solution<\/span><\/h3>\n

I have already mentioned the DFRobot Husky Lens camera, it was the subject of an earlier article and video. We have also looked at an older camera, the Pixie Lens, which was an early hobbyist-level object detection camera.<\/span><\/p>\n

These commercial cameras are more powerful than the solution that we are going to implement today with the ESP32. They have more features and they have more functions.<\/span><\/p>\n

The setup we are looking at today using the ESP32-CAM can work well if you are willing to put the effort into training it.\u00a0 It is capable of recognizing quite a few objects, especially if you pay attention to the camera mounting, focus, and lighting.<\/span><\/p>\n

But if you need a more featured or more robust solution for your application, then I suggest the Husky Lens instead.\u00a0 Today’s project is more of an exercise for those interested in learning about building object detection systems from scratch using inexpensive hardware.<\/span><\/p>\n

Object Detection<\/span><\/h2>\n

Object detection is a field of computer vision that focuses on identifying and locating objects within images or video frames. It involves identifying the presence, location, and type of one or more objects in an image.<\/span><\/p>\n

Object Detection is actually a combination of two imaging technologies:<\/span><\/p>\n