Braille Block Detection via Multi-Objective Optimization from an Egocentric Viewpoint
Abstract
:1. Introduction
- A Braille block detection framework with the egocentric images as input is proposed.
- We formulate the block detection as a multi-objective optimization problem by considering both the geometric and the appearance features.
- A Braille block detection dataset is originally built with annotations.
2. Related Work
3. Detection of Braille Block
3.1. Problem Setting and Overview
3.2. Individual Representation and Population Initialization
3.3. Objective Functions
Algorithm 1: Objective Function 1 |
Algorithm 2: Objective Function 2 |
3.4. Genetic Operators and Termination Criterion
3.5. Selection of the Final Solution
4. Experimental Results
4.1. Parameter Tuning
4.2. Performance Evaluation and Limitation Analysis
4.3. Comparison over Different MOEAs
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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MOEA | POP | GEN | SR | PR | DIV | OS | DR | DS | NS | ||
---|---|---|---|---|---|---|---|---|---|---|---|
NSGA-II [2] | 200 | 50 | 1.0 | 0.25 | - | - | - | - | - | - | - |
SPEA2 [33] | 200 | 50 | 1.0 | 0.25 | - | 200 | - | - | - | - | - |
IBEA [34] | 200 | 50 | 1.0 | 0.25 | - | - | - | - | - | - | - |
GDE3 [35] | 200 | 50 | - | - | - | - | 0.1 | 0.5 | - | - | - |
MOEA/D [36] | 200 | 50 | 1.0 | 0.25 | - | - | - | - | 20 | 0.9 | 2 |
NSGA-III [37] | 200 | 50 | 1.0 | 0.25 | 4 | - | - | - | - | - | - |
DBEA [38] | 200 | 50 | 1.0 | 0.25 | 4 | - | - | - | - | - | - |
Par. | Description |
---|---|
POP | Population size. |
GEN | Generation size. |
SR | Crossover rate of the simulated binary crossover. |
PR | Mutation rate of the polynomial mutation. |
DIV | Number of divisions. |
OS | Number of offspring generated per iteration. |
DR | Crossover rate for differential evolution. |
DS | Size of each step taken by differential evolution. |
NS | Size of the neighborhood for mating. |
Probability of mating with an individual from the neighborhood versus the entire population. | |
Maximum number of spots in the population that an offspring can replace. |
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Takano, T.; Nakane, T.; Akashi, T.; Zhang, C. Braille Block Detection via Multi-Objective Optimization from an Egocentric Viewpoint. Sensors 2021, 21, 2775. https://doi.org/10.3390/s21082775
Takano T, Nakane T, Akashi T, Zhang C. Braille Block Detection via Multi-Objective Optimization from an Egocentric Viewpoint. Sensors. 2021; 21(8):2775. https://doi.org/10.3390/s21082775
Chicago/Turabian StyleTakano, Tsubasa, Takumi Nakane, Takuya Akashi, and Chao Zhang. 2021. "Braille Block Detection via Multi-Objective Optimization from an Egocentric Viewpoint" Sensors 21, no. 8: 2775. https://doi.org/10.3390/s21082775