This week, I faced an unforeseen hardware constraint involving the Raspberry Pi. Initially, the plan was to deploy our trained card detection model directly onto a Raspberry Pi 4. However, I discovered that the Raspberry Pi 4 does not natively support multiple camera modules. Attempting to integrate two camera modules with the Raspberry Pi 4 would require using a camera multiplexer, consuming approximately 21 GPIO pins. This presented a significant obstacle since our existing design heavily depends on these GPIO pins for other critical functions, and sacrificing them was not feasible without compromising our entire design. Consequently, we could not adhere to our original plan of deploying the card detection model onto the Raspberry Pi 4.
To resolve this issue, we decided to upgrade our hardware by purchasing a Raspberry Pi 5, which inherently supports dual-camera inputs without the need for a multiplexer, thus preserving our GPIO pins. However, this upgrade led to delays, as the model deployment activities were contingent upon using our finalized hardware setup. As a result, my focus shifted from generating deployment-ready models to preparing for rapid integration once the Raspberry Pi 5 arrives.
Meanwhile, my immediate tasks include generating a custom dataset tailored specifically to our hardware environment and training the refined model promptly. Once the Raspberry Pi 5 is available, I will deploy the model to ensure our project remains aligned with our timeline.