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

arXiv: https://arxiv.org/abs/1705.05922 · HTML: https://mlsanity.com/paper/1705.05922/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2017.

## Built on by (most cited first, 7 of 7)

- [Object Detection in Autonomous Vehicles: Status and Open Challenges](https://mlsanity.com/paper/2201.07706/) (2022)
- [Vision Controlled Sensorized Prosthetic Hand](https://mlsanity.com/paper/2407.12807/) (2024)
- [Designing a Robust Low-Level Agnostic Controller for a Quadrotor with Actor-Critic Reinforcement Learning](https://mlsanity.com/paper/2210.02964/) (2022)
- [Real-time pedestrian recognition on low computational resources](https://mlsanity.com/paper/2309.01353/) (2023)
- [QWID: Quantized Weed Identification Deep neural network](https://mlsanity.com/paper/2310.18921/) (2023)
- [Fully Quantized Always-on Face Detector Considering Mobile Image Sensors](https://mlsanity.com/paper/2311.01001/) (2023)
- [Learning Adversarial Augmentation Policies for Robust Garlic Seedling Detection](https://mlsanity.com/paper/2606.26828/) (2026)

## Abstract (arXiv)

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.