Eye state detection for use in advanced driver assistance systems

Sharma, Shubham; Negi, Anuja; S, Graceline Jasmine; Vaidehi, V.; Singh, Shantanu; Raj, Dinesh Samuel Sathia; Ganesan, Subramaniam

doi:10.1109/icrtac.2018.8679348

Cited by 1 publication

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the literature [12] and [13], the authors noted that their networks could be applied under different illumination conditions. To prove the superiority of the proposed ICONet, we reproduced We reproduced the network proposed by Sharma [35] and Sharan [20]. According to Figure 8 and Table 3, our network earns a superior accuracy of at least 0.5%.…”

Section: Comparison With Other Approachesmentioning

confidence: 95%

See 1 more Smart Citation

ICONet: A Lightweight Network with Greater Environmental Adaptivity

Huang

et al. 2020

Symmetry

View full text Add to dashboard Cite

With the increasing popularity of artificial intelligence, deep learning has been applied to various fields, especially in computer vision. Since artificial intelligence is migrating from cloud to edge, deep learning nowadays should be edge-oriented and adaptive to complex environments. Aiming at these goals, this paper proposes an ICONet (illumination condition optimized network). Based on OTSU segmentation algorithm and fuzzy c-means clustering algorithm, the illumination condition classification subnet increases the environmental adaptivity of our network. The reduced time complexity and optimized size of our convolutional neural network (CNN) model enables the implementation of ICONet on edge devices. In the field of fatigue driving, we test the performance of ICONet on YawDD and self-collected datasets. Our network achieves a general accuracy of 98.56% and our models are about 590 kilobytes. Compared to other proposed networks, the ICONet shows significant success and superiority. Applying ICONet to fatigue driving detection is helpful to solve the symmetry of the needs of edge-oriented detection under complex illumination condition environments and the scarcity of related approaches.

show abstract

Section: Comparison With Other Approachesmentioning

confidence: 95%

“…The comparative results are shown in Figure 8 and Table 3. We reproduced the network proposed by Sharma [35] and Sharan [20]. According to Figure 8 and Table 3, our network earns a superior accuracy of at least 0.5%.…”

Section: Comparison With Other Approachesmentioning

confidence: 99%

ICONet: A Lightweight Network with Greater Environmental Adaptivity

Huang

et al. 2020

Symmetry

View full text Add to dashboard Cite

show abstract

Eye state detection for use in advanced driver assistance systems

Cited by 1 publication

References 14 publications

ICONet: A Lightweight Network with Greater Environmental Adaptivity

ICONet: A Lightweight Network with Greater Environmental Adaptivity

Contact Info

Product

Resources

About