CNN Classifier for Just-in-Time Woodpeckers Detection and Deterrent
This is an incremental solution for homeowners in suburban areas to prevent woodpecker damage.
The paper tackles the problem of woodpecker damage to homes by developing a CNN classifier for detecting woodpecker drumming at 25 Hz, which is implemented on an MCU using TF Lite Micro to enable an autonomous deterrent system.
Woodpeckers can cause significant damage to homes, especially in suburban areas. There are a number of preventing and repelling methods including passive decoys, though these may only provide temporary relief. Subsequently, it may be more efficient to implement a woodpecker deterrent, such as motion, light, sound, or ultrasound that would be triggered by detection of woodpecker signature drumming. To detect the typical 25 Hz drumming frequency, sampling periods under 10 milliseconds with frequent FFTs are required with considerable computational costs. An in-hardware spectrum analyzer may avoid these costs by trading off frequency for time resolutions. The trained model converted to TF Lite Micro, ported to an MCU, and identifies a variety of the prerecorded woodpecker drumming. The plan is to integrate the prototype with a deterrent device making it a completely autonomous solution.