We salute hackers who make technology useful for people in emerging markets. Leigh Johnson joined that select group when she accepted the challenge to build portable machine vision units that work offline and can be deployed for under $100 each. For hardware, a Raspberry Pi with camera plus screen can fit under that cost ceiling, and the software to give it sight is the focus of her 2018 Hackaday Superconference presentation. (Video also embedded below.)
The talk is a very concise 13 minutes, so Leigh flies through definitions of basic terms, before quickly naming TensorFlow and Keras as the tools she used. The time she saved here was spent on explaining what convolutional neural networks are and how they work, just enough to prepare the audience. But all of that is really just background, the meat of the talk is self-contained examples that Leigh has put together and made available online. I love to see that since it means you go beyond just watching and try it out for yourself. Continue reading “Leigh Johnson’s Guide To Machine Vision On Raspberry Pi”
[Roland Meertens] has a bat detector, or rather, he has a device that can record ultrasound – the type of sound that bats use to echolocate. What he wants is a bat detector. When he discovered bats living behind his house, he set to work creating a program that would use his recorder to detect when bats were around.
[Roland]’s workflow consists of breaking up a recording from his backyard into one second clips, loading them in to a Python program and running some machine learning code to determine whether the clip is a recording of a bat or not and using this to determine the number of bats flying around. He uses several Python libraries to do this including Tensorflow and LibROSA.
The Python code breaks each one second clip into twenty-two parts. For each part, he determines the max, min, mean, standard deviation, and max-min of the sample – if multiple parts of the signal have certain features (such as a high standard deviation), then the software has detected a bat call. Armed with this, [Roland] turned his head to the machine learning so that he could offload the work of detecting the bats. Again, he turned to Python and the Keras library.
With a 95% success rate, [Roland] now has a bat detector! One that works pretty well, too. For more on detecting bats and machine learning, check out the bat detector in this list of ultrasonic projects and check out this IDE for working with Tensorflow and machine learning.