Improved single shot multibox detector target detection method based on deep feature fusion

2021 ◽  
Author(s):  
Dongxu Bai ◽  
Ying Sun ◽  
Bo Tao ◽  
Xiliang Tong ◽  
Manman Xu ◽  
...  
Keyword(s):  
Target Detection ◽  
Detection Method ◽  
Feature Fusion ◽  
Single Shot ◽  
Deep Feature

scholarly journals TWC-Net: A SAR Ship Detection Using Two-Way Convolution and Multiscale Feature Mapping

2021 ◽  
Vol 13 (13) ◽  
pp. 2558
Author(s):  
Lei Yu ◽  
Haoyu Wu ◽  
Zhi Zhong ◽  
Liying Zheng ◽  
Qiuyue Deng ◽  
...  
Keyword(s):  
Target Detection ◽  
Detection Methods ◽  
Observation System ◽  
Single Shot ◽  
Small Target ◽  
Feature Mapping ◽  
Sar Images ◽  
Two Stage ◽  
Ship Detection ◽  
Deep Feature

Synthetic aperture radar (SAR) is an active earth observation system with a certain surface penetration capability and can be employed to observations all-day and all-weather. Ship detection using SAR is of great significance to maritime safety and port management. With the wide application of in-depth learning in ordinary images and good results, an increasing number of detection algorithms began entering the field of remote sensing images. SAR image has the characteristics of small targets, high noise, and sparse targets. Two-stage detection methods, such as faster regions with convolution neural network (Faster RCNN), have good results when applied to ship target detection based on the SAR graph, but their efficiency is low and their structure requires many computing resources, so they are not suitable for real-time detection. One-stage target detection methods, such as single shot multibox detector (SSD), make up for the shortage of the two-stage algorithm in speed but lack effective use of information from different layers, so it is not as good as the two-stage algorithm in small target detection. We propose the two-way convolution network (TWC-Net) based on a two-way convolution structure and use multiscale feature mapping to process SAR images. The two-way convolution module can effectively extract the feature from SAR images, and the multiscale mapping module can effectively process shallow and deep feature information. TWC-Net can avoid the loss of small target information during the feature extraction, while guaranteeing good perception of a large target by the deep feature map. We tested the performance of our proposed method using a common SAR ship dataset SSDD. The experimental results show that our proposed method has a higher recall rate and precision, and the F-Measure is 93.32%. It has smaller parameters and memory consumption than other methods and is superior to other methods.

scholarly journals A Shallow–Deep Feature Fusion Method for Pedestrian Detection

2021 ◽  
Vol 11 (19) ◽  
pp. 9202
Author(s):  
Daxue Liu ◽  
Kai Zang ◽  
Jifeng Shen
Keyword(s):  
Detection Method ◽  
Feature Fusion ◽  
Pedestrian Detection ◽  
Recall Rate ◽  
Fusion Method ◽  
Discriminative Power ◽  
Deep Feature ◽  
Proposal A ◽  
High Recall Rate ◽  
Feature Based

In this paper, a shallow–deep feature fusion (SDFF) method is developed for pedestrian detection. Firstly, we propose a shallow feature-based method under the ACF framework of pedestrian detection. More precisely, improved Haar-like templates with Local FDA learning are used to filter the channel maps of ACF such that these Haar-like features are able to improve the discriminative power and therefore enhance the detection performance. The proposed shallow feature is also referred to as weighted subset-haar-like feature. It is efficient in pedestrian detection with a high recall rate and precise localization. Secondly, the proposed shallow feature-based detection method operates as a region proposal. A classifier equipped with ResNet is then used to refine the region proposals to judge whether each region contains a pedestrian or not. The extensive experiments evaluated on INRIA, Caltech, and TUD-Brussel datasets show that SDFF is an effective and efficient method for pedestrian detection.

scholarly journals A Block Object Detection Method Based on Feature Fusion Networks for Autonomous Vehicles

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Qiao Meng ◽  
Huansheng Song ◽  
Gang Li ◽  
Yu’an Zhang ◽  
Xiangqing Zhang
Keyword(s):  
Data Augmentation ◽  
Detection Method ◽  
Feature Fusion ◽  
Autonomous Vehicle ◽  
Current Trend ◽  
Climatic Conditions ◽  
Single Shot ◽  
Deep Convolutional Neural Networks ◽  
Multi Objective ◽  
Objective Detection

Nowadays, automatic multi-objective detection remains a challenging problem for autonomous vehicle technologies. In the past decades, deep learning has been demonstrated successful for multi-objective detection, such as the Single Shot Multibox Detector (SSD) model. The current trend is to train the deep Convolutional Neural Networks (CNNs) with online autonomous vehicle datasets. However, network performance usually degrades when small objects are detected. Moreover, the existing autonomous vehicle datasets could not meet the need for domestic traffic environment. To improve the detection performance of small objects and ensure the validity of the dataset, we propose a new method. Specifically, the original images are divided into blocks as input to a VGG-16 network which add the feature map fusion after CNNs. Moreover, the image pyramid is built to project all the blocks detection results at the original objects size as much as possible. In addition to improving the detection method, a new autonomous driving vehicle dataset is created, in which the object categories and labelling criteria are defined, and a data augmentation method is proposed. The experimental results on the new datasets show that the performance of the proposed method is greatly improved, especially for small objects detection in large image. Moreover, the proposed method is adaptive to complex climatic conditions and contributes a lot for autonomous vehicle perception and planning.

scholarly journals FFESSD: An Accurate and Efficient Single-Shot Detector for Target Detection

2019 ◽  
Vol 9 (20) ◽  
pp. 4276 ◽  
Author(s):  
Wenxu Shi ◽  
Shengli Bao ◽  
Dailun Tan
Keyword(s):  
Target Detection ◽  
Feature Fusion ◽  
Single Shot ◽  
Feature Enhancement ◽  
Input Size ◽  
Pyramid Structure ◽  
Deep Layers ◽  
Extended Experiment ◽  
Conventional Structure ◽  
Better Than

The Single Shot MultiBox Detector (SSD) is one of the fastest algorithms in the current target detection field. It has achieved good results in target detection but there are problems such as poor extraction of features in shallow layers and loss of features in deep layers. In this paper, we propose an accurate and efficient target detection method, named Single Shot Object Detection with Feature Enhancement and Fusion (FFESSD), which is to enhance and exploit the shallow and deep features in the feature pyramid structure of the SSD algorithm. To achieve it we introduced the Feature Fusion Module and two Feature Enhancement Modules, and integrated them into the conventional structure of the SSD. Experimental results on the PASCAL VOC 2007 dataset demonstrated that FFESSD achieved 79.1% mean average precision (mAP) at the speed of 54.3 frame per second (FPS) with the input size 300 × 300, while FFESSD with a 512 × 512 sized input achieved 81.8% mAP at 30.2 FPS. The proposed network shows state-of-the-art mAP, which is better than the conventional SSD, Deconvolutional Single Shot Detector (DSSD), Feature-Fusion SSD (FSSD), and other advanced detectors. On extended experiment, the performance of FFESSD in fuzzy target detection was better than the conventional SSD.

scholarly journals Pedestrian and cyclist detection based on deep neural network fast R-CNN

2019 ◽  
Vol 16 (2) ◽  
pp. 172988141982965 ◽  
Author(s):  
Kelong Wang ◽  
Wei Zhou
Keyword(s):  
Neural Network ◽  
Target Detection ◽  
Deep Neural Network ◽  
Detection Method ◽  
Feature Fusion ◽  
Intelligent Vehicles ◽  
Urban Traffic ◽  
Joint Detection ◽  
Difficult Case ◽  
Important Basis

In this article, a unified joint detection framework for pedestrian and cyclist is established to realize the joint detection of pedestrian and cyclist targets. Based on the target detection of fast regional convolution neural network, a deep neural network model suitable for pedestrian and cyclist detection is established. Experiments for poor detection results for small-sized targets and complex and changeable background environment; various network improvement schemes such as difficult case extraction, multilayer feature fusion, and multitarget candidate region input were designed to improve detection and to solve the problems of frequent false detections and missed detections in pedestrian and cyclist target detection. Results of experimental verification of the pedestrian and cyclist database established in Beijing’s urban traffic environment showed that the proposed joint detection method for pedestrians and cyclists can realize the stable tracking of joint detection and clearly distinguish different target categories. Therefore, an important basis for the behavior decision of intelligent vehicles is provided.

scholarly journals IYOLO: Multi-scale infrared target detection method based on bidirectional feature fusion

2021 ◽  
Vol 1873 (1) ◽  
pp. 012020
Author(s):  
Xiaofeng Zhao ◽  
Yebin Xu ◽  
Fei Wu ◽  
Wei Cai ◽  
Zhili Zhang
Keyword(s):  
Target Detection ◽  
Detection Method ◽  
Feature Fusion ◽  
Multi Scale

Fabric defect detection based on multi-source feature fusion

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhoufeng Liu ◽  
Shanliang Liu ◽  
Chunlei Li ◽  
Bicao Li
Keyword(s):  
Defect Detection ◽  
Detection Method ◽  
Feature Fusion ◽  
New Method ◽  
Detection Accuracy ◽  
Single Shot ◽  
Content Type ◽  
Fabric Defect Detection ◽  
Promising Tool ◽  
Fabric Defect

PurposeThis paper aims to propose a new method to solve the two problems in fabric defect detection. Current state-of-the-art industrial products defect detectors are deep learning-based, which incurs some additional problems: (1) The model is difficult to train due to too few fabric datasets for the difficulty of collecting pictures; (2) The detection accuracy of existing methods is insufficient to implement in the industrial field. This study intends to propose a new method which can be applied to fabric defect detection in the industrial field.Design/methodology/approachTo cope with exist fabric defect detection problems, the article proposes a novel fabric defect detection method based on multi-source feature fusion. In the training process, both layer features and source model information are fused to enhance robustness and accuracy. Additionally, a novel training model called multi-source feature fusion (MSFF) is proposed to tackle the limited samples and demand to obtain fleet and precise quantification automatically.FindingsThe paper provides a novel fabric defect detection method, experimental results demonstrate that the proposed method achieves an AP of 93.9 and 98.8% when applied to the TILDA(a public dataset) and ZYFD datasets (a real-shot dataset), respectively, and outperforms 5.9% than fine-tuned SSD (single shot multi-box detector).Research limitations/implicationsOur proposed algorithm can provide a promising tool for fabric defect detection.Practical implicationsThe paper includes implications for the development of a powerful brand image, the development of “brand ambassadors” and for managing the balance between stability and change.Social implicationsThis work provides technical support for real-time detection on industrial sites, advances the process of intelligent manual detection of fabric defects and provides a technical reference for object detection on other industrialOriginality/valueTherefore, our proposed algorithm can provide a promising tool for fabric defect detection.

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