The performance of the image classification and object detection models remain as the most significant risks, but these will only be revealed once we start actually testing them with data collected from our camera which has not arrived yet. For now, the contingency plan would be to switch models or perhaps make the scope of our input data or images that we want to classify smaller so that the models have an easier time with recognition. One change we made to the existing design was the camera we planned on using. We initially wanted a camera with a large field of view to try and capture as much of the environment as possible, but we realized that this would make the image size too large and make recognition harder.
With regards to the object detection model development, we plan to continue developing fine-tuned YOLO models. Initial testing of pre-trained models on out-of-distribution data (BDD100k validation dataset) yielded reasonable results, but we might want to consider leaning heavier on fine-tuned models for testing such that we have models trained on a wider variety of data. There is a significant risk that fine-tuning the existing models might not even be sufficient for accurate models when we integrate and test, however, and so our contingency plan is to continue collecting and processing more diverse datasets in an effort to boost performance.
In terms of hardware, we chose to delay ordering a sound card as we are considering using bone-conduction earphones for safety. They block less ambient noise and can be connected via Bluetooth. Testing for audio can be done through the DisplayPort connector, as the audio drivers should be identical regardless of which headphones we end up choosing. For power, we have ordered a USB-C PD to 15V 5A DC Barrel Jack converter. This fits into the power requirements while allowing us to use a PD Powerbank instead of a more esoteric Powerbank with a DC output.