  Recent improvements in object detection have shown potential to aid in tasks
where previous solutions were not able to achieve. A particular area is
assistive devices for individuals with visual impairment. While
state-of-the-art deep neural networks have been shown to achieve superior
object detection performance, their high computational and memory requirements
make them cost prohibitive for on-device operation. Alternatively, cloud-based
operation leads to privacy concerns, both not attractive to potential users. To
address these challenges, this study investigates creating an efficient object
detection network specifically for OLIV, an AI-powered assistant for object
localization for the visually impaired, via micro-architecture design
exploration. In particular, we formulate the problem of finding an optimal
network micro-architecture as an numerical optimization problem, where we find
the set of hyperparameters controlling the MobileNetV2-SSD network
micro-architecture that maximizes a modified NetScore objective function for
the MSCOCO-OLIV dataset of indoor objects. Experimental results show that such
a micro-architecture design exploration strategy leads to a compact deep neural
network with a balanced trade-off between accuracy, size, and speed, making it
well-suited for enabling on-device computer vision driven assistive devices for
the visually impaired.
