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.
翻译:最近对天体探测的改进表明,在以往解决方案无法实现的任务中,有可能帮助完成最近对天体探测的改进。一个特定领域是为视力受损的个人提供的辅助装置。虽然最先进的深神经网络已证明能够达到高水平的天体探测性能,但其高计算和内存要求使其在安装装置操作方面成本过高。或者,云基操作导致隐私问题,两者对潜在用户都不具有吸引力。为应对这些挑战,本研究调查专门为OLIV,一个用于视障者天体定位的AI-动力助手,通过微结构设计探索,建立一个高效的天体探测网络。特别是,我们把寻找最佳网络微结构作为数字优化问题,我们发现控制移动NetV2-SSD网络的超参数组,使室内物体MSCOCO-OLIV数据集的经修改的网络核心目标功能最大化。实验结果表明,这样的微结构设计探索战略导致一个紧凑的深神经网络,在计算机驱动的精确度、大小、速度和速度之间形成平衡的贸易辅助性定位,从而在计算机驱动的视像能推进装置之间形成平衡的定位。