Title: LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems,
Abstract: 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.