A Comprehensive and Systematic Review of Deep Learning Based Object Recognition Techniques for the Visually Impaired

Aakriti Kinra, Wamica Walia, S. Sharanya
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引用次数: 1

Abstract

Object detection is one of computer vision's most essential and challenging visual recognition tasks. The primary focus is to detect objects belonging to different classes and assign each one of them a predefined class label. The dearth of support at diagnosis centers, restricted access to actions and data, stigma created by society, and lack of employment frequently lead visually challenged individuals to seclusion. Existing technologies include A Path Force Feedback Belt (PF belt), Eye Substitution, and Radio Frequency Identification Walking Stick (RFIWS). They detect and localize objects to offer the visually impaired an understanding of the environment around them utilizing the operations of sensors. These systems do not provide real-time responses and are suitable only for outdoor coverage. Due to ultrasonic or radio frequency limitations, the detection range of these systems is between 1m to 5m. The primary intent of this work is to deliver a complete and extensive analysis of the research done in the field of object recognition for VI-challenged people.
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基于深度学习的视障对象识别技术的全面系统综述
目标检测是计算机视觉中最重要、最具挑战性的视觉识别任务之一。主要重点是检测属于不同类的对象,并为每个对象分配预定义的类标签。诊断中心缺乏支持、获取行动和数据受到限制、社会造成的耻辱以及缺乏就业,往往导致视力障碍患者被隔离。现有的技术包括路径力反馈带(PF带),眼睛替代和射频识别手杖(RFIWS)。他们检测和定位物体,让视障人士了解周围的环境,利用传感器的操作。这些系统不提供实时响应,只适用于室外覆盖。由于超声波或射频的限制,这些系统的检测范围在1m到5m之间。这项工作的主要目的是提供一个完整的和广泛的研究在对象识别领域为vi挑战的人做了分析。
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