Deep convolutional Neural Networks (CNN) are the state-of-the-art performers
for object detection task. It is well known that object detection requires more
computation and memory than image classification. Thus the consolidation of a
CNN-based object detection for an embedded system is more challenging. In this
work, we propose LCDet, a fully-convolutional neural network for generic object
detection that aims to work in embedded systems. We design and develop an
end-to-end TensorFlow(TF)-based model. Additionally, we employ 8-bit
quantization on the learned weights. We use face detection as a use case. Our
TF-Slim based network can predict different faces of different shapes and sizes
in a single forward pass. Our experimental results show that the proposed
method achieves comparative accuracy comparing with state-of-the-art CNN-based
face detection methods, while reducing the model size by 3x and memory-BW by
~4x comparing with one of the best real-time CNN-based object detector such as
YOLO. TF 8-bit quantized model provides additional 4x memory reduction while
keeping the accuracy as good as the floating point model. The proposed model
thus becomes amenable for embedded implementations.

本文提出了一种 LCDet 的全卷积神经网络，旨在用于嵌入式系统上的对象检测。作者设计并开发了一个端到端的 TensorFlow (TF) 模型，并采用 8 位量化技术。实验结果显示，该模型在保持准确率的同时，将模型大小和内存带宽分别减少了 3 倍和 4 倍，可以在嵌入式系统上实现。