A Neural Processing Unit-Accelerated Monocular Perception Pipeline for Remotely Operated Underwater Vehicle Station Keeping

Abstract

Holding a stable viewpoint is a prerequisite for observation tasks performed by remotely operated underwater vehicles, yet it is difficult to achieve when the only sensor is a monocular camera and all computation must run on board. This work reports the deployment of a complete visual stationkeeping perception pipeline on a Qualcomm QCS8550 platform using its Hexagon HTP/NPU accelerator, in which two networks---an INT8 object detector and a dense monocular depth model---share a single accelerator. A per-stage latency breakdown shows that the throughput difference between an accelerated and a CPU-executed configuration is attributable to one stage: moving depth inference alone to the accelerator makes it 27 times faster and carries the pipeline from 0.46 to 8.47 frames per second, with depth inference still occupying two thirds of the frame budget afterwards and the accelerator occupied at least 69\% of wall-clock time. We document two deployment failure modes that are silent rather than loud: an output polarity that must be established empirically, and a normalisation choice that destroys the range signal. The two available range cues are combined by an analytic fusion rule---the square root of the tracked bounding box area and the depth output both vary inversely with distance, so the two are directly combinable once anchored on a reference captured at target selection, without training data or knowledge of the target dimensions. On the rigid targets evaluated here the zero-cost geometric term carries more of the distance signal than the depth network that consumes two thirds of the frame budget, and the two cues degrade unequally on immersion, which makes their relative weighting an operating parameter rather than a constant. Evaluation is open-loop; closed-loop validation was prevented by a degenerate thruster configuration.