# LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems

## TL;DR

LCDet is a fully-convolutional neural network designed for efficient object detection on embedded systems, achieving comparable accuracy to state-of-the-art methods while significantly reducing model size and memory usage through quantization.

## Contribution

The paper introduces LCDet, a low-complexity, fully-convolutional CNN for object detection in embedded systems, utilizing 8-bit quantization for further efficiency.

## Key findings

- Model size reduced by 3x compared to YOLO
- Memory bandwidth reduced by approximately 4x
- Quantized model maintains accuracy with floating point model

## 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.