Staircase Detection Using a Lightweight Look-Behind Fully Convolutional Neural Network

Diamantis, Dimitrios E.; Koutsiou, Dimitra-Christina C.; Iakovidis, Dimitris K.

doi:10.1007/978-3-030-20257-6_45

Cited by 13 publications

(13 citation statements)

References 15 publications

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“…Table 3. Comparative classification performance results between the LB-FCN light architecture [45] and the MobileNet-v2 architecture [64].…”

Section: Discussionmentioning

confidence: 99%

“…Networks, such as VGGNet [40], GoogLeNet [65], and ResNet [66] provide high classification accuracy but with ever more increasing computational complexity, the result of which limits their usage on high-end devices equipped with expensive GPUs and low inference time [67]. Aiming to decrease the computational complexity and maintain high object recognition performance, this work demonstrated that the LB-FCN light [45] architecture can be used as an effective object recognition solution in the field of obstacle recognition. Furthermore, the comparative results presented in Section 5.2 exhibited that the LB-FCN light architecture is able to achieve higher generalization performance and maintain lower computational complexity compared to the state-of-the-art MobileNet-v2 architecture [64].…”

Section: Discussionmentioning

confidence: 99%

“…In VPS, we make use of a state-of-the-art CNN architecture named Look Behind Fully Convolutional Network light or LB-FCN light [45], which offers high object recognition accuracy, while maintaining low computational complexity. Its architecture is based on the original LB-FCN architecture [46], which offers multi-scale feature extraction and shortcut connections that enhance the overall object recognition capabilities.…”

Section: Object Recognitionmentioning

confidence: 99%

“…For the object recognition task, the LB-FCN light network architecture [45] was chosen, since it has been proven to work well on obstacle detection-related tasks. A key characteristic of the architecture is the relatively low number of free-parameters compared to both conventional CNN architectures, such as [40], and mobile-oriented architectures, such as [41,42].…”

Section: Obstacle Recognitionmentioning

confidence: 99%

“…Comparative classification performance results between the LB-FCN light architecture[45] and the MobileNet-v2 architecture[64]. Computational complexity comparison between the LB-FCN light architecture[3] and the MobileNet-v2 architecture[65].…”

mentioning

confidence: 99%

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Uncertainty-Aware Visual Perception System for Outdoor Navigation of the Visually Challenged

Dimas

Diamantis

Kalozoumis

et al. 2020

Sensors

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Every day, visually challenged people (VCP) face mobility restrictions and accessibility limitations. A short walk to a nearby destination, which for other individuals is taken for granted, becomes a challenge. To tackle this problem, we propose a novel visual perception system for outdoor navigation that can be evolved into an everyday visual aid for VCP. The proposed methodology is integrated in a wearable visual perception system (VPS). The proposed approach efficiently incorporates deep learning, object recognition models, along with an obstacle detection methodology based on human eye fixation prediction using Generative Adversarial Networks. An uncertainty-aware modeling of the obstacle risk assessment and spatial localization has been employed, following a fuzzy logic approach, for robust obstacle detection. The above combination can translate the position and the type of detected obstacles into descriptive linguistic expressions, allowing the users to easily understand their location in the environment and avoid them. The performance and capabilities of the proposed method are investigated in the context of safe navigation of VCP in outdoor environments of cultural interest through obstacle recognition and detection. Additionally, a comparison between the proposed system and relevant state-of-the-art systems for the safe navigation of VCP, focused on design and user-requirements satisfaction, is performed.

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“…Table 3. Comparative classification performance results between the LB-FCN light architecture [45] and the MobileNet-v2 architecture [64].…”

Section: Discussionmentioning

confidence: 99%