دانلود رایگان مقاله انگلیسی تشخیص تصویر لمسی چندگانه بر اساس یادگیری عمیق - امرالد 2018

عنوان فارسی
تشخیص تصویر لمسی چندگانه بر اساس یادگیری عمیق
عنوان انگلیسی
Multi-modal haptic image recognition based on deep learning
صفحات مقاله فارسی
0
صفحات مقاله انگلیسی
9
سال انتشار
2018
نشریه
امرالد - Emerald
فرمت مقاله انگلیسی
PDF
کد محصول
E6426
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مهندسی کامپیوتر
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هوش مصنوعی
مجله
بررسی سنسور - Sensor Review
دانشگاه
Nanjing University of Aeronautics and Astronautics - Nanjing - China
کلمات کلیدی
یادگیری عمیق، ادغام داده ها، درک هپتیک، تصاویر هتیک چند مدل
چکیده

Abstract


Purpose – This paper aims to improve the diversity and richness of haptic perception by recognizing multi-modal haptic images. Design/methodology/approach – First, the multi-modal haptic data collected by BioTac sensors from different objects are pre-processed, and then combined into haptic images. Second, a multi-class and multi-label deep learning model is designed, which can simultaneously learn four haptic features (hardness, thermal conductivity, roughness and texture) from the haptic images, and recognize objects based on these features. The haptic images with different dimensions and modalities are provided for testing the recognition performance of this model. Findings – The results imply that multi-modal data fusion has a better performance than single-modal data on tactile understanding, and the haptic images with larger dimension are conducive to more accurate haptic measurement. Practical implications – The proposed method has important potential application in unknown environment perception, dexterous grasping manipulation and other intelligent robotics domains. Originality/value – This paper proposes a new deep learning model for extracting multiple haptic features and recognizing objects from multimodal haptic images.

نتیجه گیری

7. Conclusion


A new multi-class and multi-label deep learning model is designed for recognizing haptic images. The model can simultaneously extract four haptic features and recognize objects from the haptic images with multi-modal signals. The results show that: compared with single-modal haptic signals, multi-modal signal fusion can successfully improve the recognition performance; increasing the dimension of haptic images is also beneficial for better recognition performance. The proposed model can provide more accurate and richer haptic information, and improve the capabilities of unknown environment perception and dexterous manipulation.


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