This #MagPiMonday, we’re hoping to inspire you to add artificial intelligence to your Raspberry Pi designs with this feature by Phil King, from the latest issue of The MagPi.

With their powerful AI accelerator modules, Raspberry Pi’s Camera Module and AI Kit open up exciting possibilities in computer vision and machine learning. The versatility of the Raspberry Pi platform, combined with AI capabilities, opens up a world of new possibilities for innovative smart projects. From creative experiments to practical applications like smart pill dispensers, makers are harnessing the kit’s potential to push the boundaries of AI. In this feature, we explore some standout projects, and hope they inspire you to embark on your own.

Peeper Pam boss detector

By VEEB Projects

AI computer vision can identify objects within a live camera view. In this project, VEEB’s Martin Spendiff and Vanessa Bradley have used it to detect humans in the frame, so you can tell if your boss is approaching behind you as you sit at your desk!

The project comprises two parts. A Raspberry Pi 5 equipped with a Camera Module and AI Kit handles the image recognition and also acts as a web server. This uses web sockets to send messages wirelessly to the ‘detector’ part — a Raspberry Pi Pico W and a voltmeter whose needle moves to indicate the level of AI certainty for the ID.

Having got their hands on an AI Kit — “a nice intro into computer vision” — it took the pair just three days to create Peeper Pam. “The most challenging bit was that we’d not used sockets — more efficient than the Pico constantly asking Raspberry Pi ‘do you see anything?’,” says Martin. “Raspberry Pi does all the heavy lifting, while Pico just listens for an ‘I’ve seen something’ signal.”

While he notes that you could get Raspberry Pi 5 to serve both functions, the two-part setup means you can place the camera in a different position to monitor a spot you can’t see. Also, by adapting the code from the project’s GitHub repo, there are lots of other uses if you get the AI to deter other objects. “Pigeons in the window box is one that we want to do,” Martin says.

Monster AI Pi PC

By Jeff Geerling

Never one to do things by halves, Jeff Geerling went overboard with Raspberry Pi AI Kit and built a Monster AI Pi PC with a total of eight neural processors. In fact, with 55 TOPS (trillions of operations per second), it’s faster than the latest AMD, Qualcomm, and Apple Silicon processors!

The NPU chips — including the AI Kit’s Hailo-8L — are connected to a large 12× PCIe slot card with a PEX 8619 switch capable of handling 16 PCI Express Gen 2 lanes. The card is then mounted on a Raspberry Pi 5 via a Pineboards uPCIty Lite HAT, which has an additional 12V PSU to supply the extra wattage needed for all those processors.

With a bit of jiggery-pokery with the firmware and drivers on Raspberry Pi, Jeff managed to get it working.

Car detection & tracking system

By Naveen

As a proof of concept, Japanese maker Naveen aimed to implement an automated system for identifying and monitoring cars at toll plazas to get an accurate tally of the vehicles entering and exiting.

With the extra processing power provided by a Raspberry AI Kit, the project uses Edge Impulse computer vision to detect and count cars in the view from a Camera Module Wide. “We opted for a wide lens because it can capture a larger area,” he says, “allowing the camera to monitor multiple lanes simultaneously.” He also needed to train and test a YOLOv5 machine learning model. All the details can be found on the project page via the link above, which could prove useful for learning how to train custom ML models for your own AI project.

Safety helmet detection system

By Shakhizat Nurgaliyev

Wearing a safety helmet on a building site is essential and could save your life. This computer vision project uses Raspberry Pi AI Kit with the advanced YOLOv8 machine learning model to quickly and accurately identify objects within the camera view, running at an impressive inference speed of 30fps.

The project page has a guide showing how to make use of Raspberry Pi AI Kit to achieve efficient AI inferencing for safety helmet detection. This includes details of the software installation and model training process, for which the maker has provided a link to a dataset of 5000 images with bounding box annotations for three classes: helmet, person, and head.

Accelerating MediaPipe models

By Mario Bergeron

Google’s MediaPipe is an open-source framework developed for building machine learning pipelines, especially useful for working with videos and images.

Having used MediaPipe on other platforms, Mario Bergeron decided to experiment with it on a Raspberry Pi AI Kit. On the project page (linked above) he details the process, including using his Python demo application with options to detect hands/palms, faces, or poses.

Mario’s test results show how much better the AI Kit’s Hailo-8L AI accelerator module performs compared to running reference TensorFlow Lite models on Raspberry Pi 5 alone: up to 5.8 times faster. With three models running for hand and landmarks detection, the frame rate is 26–28fps with one hand detected, and 22–25fps for two.

The MagPi #147 out NOW!

You can grab the new issue right now from Tesco, Sainsbury’s, Asda, WHSmith, and other newsagents, including the Raspberry Pi Store in Cambridge. It’s also available at our online store, which ships around the world. You can also get it via our app on Android or iOS.

You can also subscribe to the print version of The MagPi. Not only do we deliver it globally, but people who sign up to the six- or twelve-month print subscription get a FREE Raspberry Pi Pico W!

The post Raspberry Pi AI Kit projects appeared first on Raspberry Pi.

AI models are adept at distinguishing one winged creature from another. This #MagPiMonday, Rosie Hattersley goes beyond the buzz.

Fun fact that might get you a point in the local pub quiz: Vespa, Piaggio’s iconic scooter, is Italian for wasp, which its buzzing engine sounds a bit like. Less fun fact: nature’s counterpart to the speedy two-wheeler has an aggressive variant that has been seen in increasing numbers across western Europe and which is a direct threat to bees, which are one of their key food sources. Bees are great for biodiversity; Asian hornets (the largest type of eusocial wasp) are not. But it’s only particular hornet species that pose such a threat. Most citizen reports of Asian hornets are native species, and a key issue is ensuring that existing hornet species are not being destroyed on this mistaken assumption. To combat misinformation and alarm at the so-called ‘killer’ hornet (itself a subset of wasp), academics at the University of Exeter have developed a VespAI detector that presents a positive identification system showing where new colonies of the invasive hornet Vespa velutina nigrithorax have begun to spread. The system works by drawing the insects to a pad that is impregnated with tasty (to wasps) smelling foodstuffs.

Considerate response

VespAI provides a nonharmful alternative to traditional trapping surveys and can also be used for monitoring hornet behaviour and mapping distributions of both the Asian hornet (Vespa velutina) and European hornet (Vespa crabro), which is protected in some countries. “Live hornets can be caught and tracked back to the nest, which is the only effective way to destroy them,” explains the team’s research paper.

Creepy feeling

VespAI features a camera positioned above a bait station that detects insects as they land to feed and gets to work establishing whether the curious mite is, in fact, an Asian hornet. The Exeter team developed the AI algorithm in Python, using YOLO image detection models. These identify whether Asian hornets are present and, if so, send an alert to users. Raspberry Pi proved a great choice because of its compact size, ability to run the hornet recognition algorithm, real-time clock, and support for peripherals such as an external battery. The prototype bait station design was made with items that the team had at hand in their lab, including a squirrel baffle for the weather shield, Petri dishes and sponges to hold hornet attractant, and a beehive stand for the monitor to rest on.

Design challenges included optimising the hornet detection algorithm for use on Raspberry Pi. “An AI algorithm may work well during training or when validated in the lab. However, field deployment is essential to expose it to potentially unforeseen scenarios that may return errors”, they note. The project also involved developing a monitor with an integrated camera, processor, and peripherals while minimising power consumption. To this end, the VespAI team is currently optimising their software to run on Raspberry Pi Zero, having watched footage of the AntVideoRecord device monitoring leafcutter ant (Acromyrmex lundi) foraging trails and been impressed by its ability to run for extended periods remotely due to its low power consumption.

As this interactive map shows, Asian hornets have quickly made inroads across Western Europe.

The Raspberry Pi-enabled setup is “intended to support national surveillance efforts, thus limiting hornet incursions into new regions,” explains Dr Thomas O’Shea-Wheller, a research fellow in the university’s Environment and Sustainability Institute. He and his colleagues have been working on the AI project since 2022, conducting additional fieldwork this summer with the National Bee Unit and the Government of Jersey (Channel Islands) mapping new locations and fine-tuning its accessibility to potential users ahead of a planned commercial version. 

Given Raspberry Pi’s extensive and enthusiastic users, they hope sharing their code on GitHub will help expand the number of VespAI detection stations and improve surveillance and reporting of hornet species.

This article originally featured in issue 146 of The MagPi magazine.

The MagPi #146 out NOW!

You can grab the new issue right now from Tesco, Sainsbury’s, Asda, WHSmith, and other newsagents, including the Raspberry Pi Store in Cambridge. It’s also available at our online store, which ships around the world. You can also get it via our app on Android or iOS.

You can also subscribe to the print version of The MagPi. Not only do we deliver it globally, but people who sign up to the six- or twelve-month print subscription get a FREE Raspberry Pi Pico W!

The post Track Asian hornets with VespAI | #MagPiMonday appeared first on Raspberry Pi.