تقرير
Efficient and Low-Footprint Object Classification using Spatial Contrast
| العنوان: | Efficient and Low-Footprint Object Classification using Spatial Contrast |
|---|---|
| المؤلفون: | Belding, Matthew, Stumpp, Daniel C., Kubendran, Rajkumar |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Neural and Evolutionary Computing, Electrical Engineering and Systems Science - Image and Video Processing, I.4 |
| الوصف: | Event-based vision sensors traditionally compute temporal contrast that offers potential for low-power and low-latency sensing and computing. In this research, an alternative paradigm for event-based sensors using localized spatial contrast (SC) under two different thresholding techniques, relative and absolute, is investigated. Given the slow maturity of spatial contrast in comparison to temporal-based sensors, a theoretical simulated output of such a hardware sensor is explored. Furthermore, we evaluate traffic sign classification using the German Traffic Sign dataset (GTSRB) with well-known Deep Neural Networks (DNNs). This study shows that spatial contrast can effectively capture salient image features needed for classification using a Binarized DNN with significant reduction in input data usage (at least 12X) and memory resources (17.5X), compared to high precision RGB images and DNN, with only a small loss (~2%) in macro F1-score. Binarized MicronNet achieves an F1-score of 94.4% using spatial contrast, compared to only 56.3% when using RGB input images. Thus, SC offers great promise for deployment in power and resource constrained edge computing environments. Comment: 6 pages, 6 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2311.03422 |
| رقم الأكسشن: | edsarx.2311.03422 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |