Keeping floating-point precision because INT8 flattened the near field is a more useful result than assuming quantization is always the right optimization. For the Sonar mode, I would be careful about turning the average depth of each half into a distance-like cue: relative monocular depth does not establish a stable metric scale by itself.
A nearby narrow obstacle can also disappear into that average when the background occupies most pixels. Testing a lower-depth percentile within the walking corridor, alongside explicit frame-age and invalid-output checks, would expose that failure mode. The heatmap can look convincing while the audible cue misses the object that matters most.
Ahmet Özel
AI Engineer. Computer Vision, RAG and LLM agents.
Keeping floating-point precision because INT8 flattened the near field is a more useful result than assuming quantization is always the right optimization. For the Sonar mode, I would be careful about turning the average depth of each half into a distance-like cue: relative monocular depth does not establish a stable metric scale by itself.
A nearby narrow obstacle can also disappear into that average when the background occupies most pixels. Testing a lower-depth percentile within the walking corridor, alongside explicit frame-age and invalid-output checks, would expose that failure mode. The heatmap can look convincing while the audible cue misses the object that matters most.