Ariyan Azami | Searcharxiv

arXiv2026

From White Canes to Edge AI: A Survey of Assistive Navigation Technologies for Blind and Low-Vision Individuals

Independent mobility is a foundational requirement for the autonomy of blind and low-vision (BLV) individuals, yet the assistive technologies that support it have historically evolved slowly. This survey systematically reviews 108 peer-reviewed studies published between 2008 and 2026, organizing the field into four technological eras: (i) a foundational era of passive mechanical aids, namely the white cane and the guide dog; (ii) a hardware era of electronic travel aids built on ultrasonic, infrared, and satellite positioning sensors that alert users to proximity but lack semantic understanding; (iii) a deep learning era in which convolutional and transformer-based detectors introduced context-aware scene understanding, initially tethered to cloud infrastructure; and (iv) a contemporary edge-native era defined by model compression, quantization, hardware acceleration, and multi-sensor fusion that relocate intelligence onto resource-constrained wearable and mobile devices. For each era we analyze representative systems, sensing modalities, computational architectures, and reported performance, and we consolidate the field's model-optimization techniques into a single technical reference spanning four detector families and twelve compression strategies. Across the corpus, we find a persistent mismatch between rapid algorithmic progress and slow real-world adoption, driven by cognitive overload in user interfaces, fragmented evaluation protocols, and the scarcity of studies involving BLV participants. We conclude with open challenges, including seamless indoor-outdoor transition, standardized benchmarks, privacy-preserving perception, and the emerging role of multimodal foundation models, and outline a roadmap for socially acceptable, user-centered assistive navigation.

cs.HC↗

arXiv2026

ROD-Dataset: A Pedestrian-Perspective Benchmark of Urban Obstacle Classes for YOLO-Based Detection

Two groups of pedestrians face avoidable risk on the city sidewalk: people living with visual impairment, who reach an obstacle before anything warns them, and people walking with divided attention, who register a hazard late or not at all. A vision system can warn them only about obstacles it has learned to recognize, so its training data limits the protection it offers. No suitable training set exists: public benchmarks are recorded from vehicles, aerial platforms, or fixed rigs, general-purpose detectors omit the street furniture that causes trips, and pedestrian-focused collections are confined to single regions with incompatible class schemes. This paper introduces the Real-time Obstacle Detection (ROD) dataset: more than 24,000 images and 40,000 annotated obstacles across 25 classes, from vehicles and people to the stairs, manholes, traffic cones, and benches that general-purpose detectors overlook. Because street furniture differs between cities, ROD unifies twenty-nine public collections onto one taxonomy and adds eye-level imagery from two contrasting cities on two continents, Tehran and Toronto. Every label is human-verified, and each capture campaign is admitted only after a compact detector shows it can learn the class. Six lightweight YOLO detectors (nano and tiny variants) benchmarked on ROD all exceed 0.86 recall; YOLO12n attains the highest recall (0.889), YOLO26n the highest precision (0.925), and YOLOv9t the best mAP@0.50 (0.882) with the fewest parameters. Because the recall and mAP rankings disagree, selecting a model by mAP does not minimize missed obstacles, so per-class recall is reported for all 25 classes.

cs.CV↗