Binary Neural Networks for Object Detection

Binary Neural Networks for Object Detection

Wang, Yizhou (TU Delft Electrical Engineering, Mathematics and Computer Science)

Al-Ars, Zaid (mentor)
Pan, Wei (graduation committee)
van Genderen, Arjan (graduation committee)
Zhu, Baozhou (graduation committee)

Delft University of Technology

Embedded Systems

2019-08-29

In the past few years, convolutional neural networks (CNNs) have been widely utilized and shown state-of-the-art performances on computer vision tasks. However, CNN based approaches usually require a large amount of storage, run-time memory, as well as computation power in both training and inference time, which are usually used on GPU based machines to ensure the speed for inferences. But they are usually insufficient to be deployed on low-power applications. Although many approaches were proposed to compress and accelerate the CNN models, most of them were only evaluated on relatively simple problems (e.g. image classification), which only support limited real-world applications. Especially, among those methods, binary quantization can achieve very high model compression, but only a few works have been observed to utilize it on more complex tasks. Therefore, the exploration and evaluations of applying binary quantization on more complex tasks like object detection are worthwhile, which can be used in much more applications like autonomous driving and face detection. In this project, we apply and evaluate two different binary quantization approaches, named ABC-Net and PA-Net on object detection tasks. Also, we specify the exact implementation details for the binary convolutional operations in this project. As a result, we can achieve maximally 6.1× (around 16% of the full-precision model) compression, and minimal 2.5% accuracy reduction for weight quantization. The weight quantized models were able to outperform some existing real-time detectors in terms of both accuracy and storage size. Although large accuracy reduction was observed for input quantization, the quantized model could still maintain an acceptable accuracy compared to existing real-time object detectors.

Neural Network Quantization
Deep Learning
Object Detection
Computer Vision
Artificial Intelligence

http://resolver.tudelft.nl/uuid:9f0da106-82ea-4f2e-9cd5-8bc834885d6f

2020-09-01

Student theses

master thesis

© 2019 Yizhou Wang