Deep learning based object detection for resource constrained devices: Systematic review, future trends and challenges ahead

Automatic animal identification from drone camera based on point pattern analysis of herd behaviour

Identification of maceral groups in Chinese bituminous coals based on semantic segmentation models

Abstract Deep neural networks (DNNs) have achieved great success in many object detection tasks. However, such DNNS-based large object detection models are generally computationally expensive and memory intensive. It is difficult to deploy them to devices with low memory resources or scenarios with high real-time requirements, which greatly limits their application and promotion. In recent years, many researchers have focused on compressing large object detection models without significantly degrading their performance, and have made great progress. Therefore, this paper presents a survey of object detection model compression techniques in recent years. Firstly, these compression techniques were divided into six categories: network pruning, lightweight network design, neural architecture search (NAS), low-rank decomposition, network quantization, and Knowledge distillation (KD) methods. For each category, we select some representative state-of-the-art methods and compare and analyze their performance on public datasets. After that, we discuss the application scenarios and future directions of model compression techniques. Finally, this paper is further concluded by analyzing the advantages and disadvantages of six types of model compression techniques. 

A survey of model compression strategies for object detection

/pdf/a-region-based-efficient-network-for-accurate-object-14duwq2cd0.pdf

A Region-Based Efficient Network for Accurate Object Detection

The huge computational overhead limits the inference of convolutional neural networks on mobile devices for object detection, which plays a critical role in many real-world scenes, such as face identification, autonomous driving, and video surveillance. To solve this problem, this paper introduces a lightweight convolutional neural network, called RSANet: Towards Real-time Object Detection with Residual Semantic-guided Attention Feature Pyramid Network. Our RSANet consists of two parts: (a) Lightweight Convolutional Network (LCNet) as backbone, and (b) Residual Semantic-guided Attention Feature Pyramid Network (RSAFPN) as detection head. In the LCNet, in contrast to recent advances of lightweight networks that prefer to utilize pointwise convolution for changing the number of feature maps, we design a Constant Channel Module (CCM) to save the Memory Access Cost (MAC) and design Down Sampling Module (DSM) to save the computational cost. In the RSAFPN, meanwhile, we employ Residual Semantic-guided Attention Mechanism (RSAM) to fuse the multi-scale features from LCNet for improving detection performance efficiently. The experiment results show that, on PASCAL VOC 20007 dataset, RSANet only requires 3.24 M model size and needs only 3.54B FLOPs with a 416×416 input image. Compared to YOLO Nano, our method obtains a 6.7% improvement in accuracy and requires less computation. On MS COCO dataset, RSANet only requires 4.35 M model size and needs only 2.34B FLOPs with a 320×320 input image. Our method obtains a 1.3% improvement in accuracy compared to Pelee. The comprehensive experiment results demonstrate that our model achieves promising results in terms of available speed and accuracy trade-off.

RSANet: Towards Real-Time Object Detection with Residual Semantic-Guided Attention Feature Pyramid Network

For image semantic segmentation, a fully convolutional network is usually employed as the encoder to abstract visual features of the input image. A meticulously designed decoder is used to decoding the final feature map of the backbone. The output resolution of backbones which are designed for image classification task is too low to match segmentation task. Most existing methods for obtaining the final high-resolution feature map can not fully utilize the information of different layers of the backbone. To adequately extract the information of a single layer, the multi-scale context information of different layers, and the global information of backbone, we present a new attention-augmented module named Dense-attention Context Module (DCM), which is used to connect the common backbones and the other decoding heads. The experiments show the promising results of our method on Cityscapes dataset.

DCM: A Dense-Attention Context Module For Semantic Segmentation

The invention discloses a real-time image semantic segmentation method and system based on a lightweight convolutional neural network The system comprises a down-sampling unit, an up-sampling unit and an extreme efficient residual module The down-sampling unit reduces the resolution and changes the number of channels; the up-sampling unit improves the resolution and changes the number of channels; the extreme efficient residual error module is composed of 1D decomposition convolution, efficient depth separable convolution and expansion convolution with different expansion rates, and is usedfor extracting features The overall network architecture disclosed by the invention is an efficient asymmetric multi-branch encoding and decoding structure; compared with the existing most advanced lightweight network model, the network architecture and the segmentation method provided by the invention have the advantages that the optimal balance between the segmentation precision and the implementation efficiency is realized, and the network architecture and the segmentation method become an effective method for solving a real-time image semantic segmentation task

Real-time image semantic segmentation method and system based on lightweight convolutional neural network

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Li Shenghua

Author Tools

Chat about Author

Papers

RSANet: Towards Real-Time Object Detection with Residual Semantic-Guided Attention Feature Pyramid Network

DCM: A Dense-Attention Context Module For Semantic Segmentation

Real-time image semantic segmentation method and system based on lightweight convolutional neural network