Small Object Detection Using Deep Feature Pyramid Networks

2018 ◽  
pp. 554-564 ◽  
Author(s):  
Zhenwen Liang ◽  
Jie Shao ◽  
Dongyang Zhang ◽  
Lianli Gao
Keyword(s):  
Object Detection ◽  
Small Object ◽  
Deep Feature ◽  
Feature Pyramid ◽  
Small Object Detection

Small object detection combining attention mechanism and a novel FPN

2021 ◽  
pp. 1-13
Author(s):  
Junying Chen ◽  
Shipeng Liu ◽  
Liang Zhao ◽  
Dengfeng Chen ◽  
Weihua Zhang
Keyword(s):  
Object Detection ◽  
Small Object ◽  
Extraction Ability ◽  
Feature Pyramid ◽  
Face Datasets ◽  
New Feature ◽  
Small Object Detection ◽  
Low Sensitivity ◽  
Objects Detection ◽  
Transmission Ability

Since small objects occupy less pixels in the image and are difficult to recognize. Small object detection has always been a research difficulty in the field of computer vision. Aiming at the problems of low sensitivity and poor detection performance of YOLOv3 for small objects. AFYOLO, which is more sensitive to small objects detection was proposed in this paper. Firstly, the DenseNet module is introduced into the low-level layers of backbone to enhance the transmission ability of objects information. At the same time, a new mechanism combining channel attention and spatial attention is introduced to improve the feature extraction ability of the backbone. Secondly, a new feature pyramid network (FPN) is proposed to better obtain the features of small objects. Finally, ablation studies on ImageNet classification task and MS-COCO object detection task verify the effectiveness of the proposed attention module and FPN. The results on Wider Face datasets show that the AP of the proposed method is 11.89%higher than that of YOLOv3 and 8.59%higher than that of YOLOv4. All of results show that AFYOLO has better ability for small object detection.

scholarly journals Transformed Dynamic Feature Pyramid for Small Object Detection

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 134649-134659
Author(s):  
Hong Liang ◽  
Ying Yang ◽  
Qian Zhang ◽  
Linxia Feng ◽  
Jie Ren ◽  
...  
Keyword(s):  
Object Detection ◽  
Dynamic Feature ◽  
Small Object ◽  
Feature Pyramid ◽  
Small Object Detection

scholarly journals Small Object Detection Algorithm Based on Feature Pyramid-Enhanced Fusion SSD

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Haotian Li ◽  
Kezheng Lin ◽  
Jingxuan Bai ◽  
Ao Li ◽  
Jiali Yu
Keyword(s):  
Object Detection ◽  
Detection Rate ◽  
Detection Algorithm ◽  
Single Shot ◽  
Small Object ◽  
Feature Maps ◽  
Scale Invariant ◽  
Feature Pyramid ◽  
Small Object Detection ◽  
Detection And Localization

In order to improve the detection rate of the traditional single-shot multibox detection algorithm in small object detection, a feature-enhanced fusion SSD object detection algorithm based on the pyramid network is proposed. Firstly, the selected multiscale feature layer is merged with the scale-invariant convolutional layer through the feature pyramid network structure; at the same time, the multiscale feature map is separately converted into the channel number using the scale-invariant convolution kernel. Then, the obtained two sets of pyramid-shaped feature layers are further feature fused to generate a set of enhanced multiscale feature maps, and the scale-invariant convolution is performed again on these layers. Finally, the obtained layer is used for detection and localization. The final location coordinates and confidence are output after nonmaximum suppression. Experimental results on the Pascal VOC 2007 and 2012 datasets confirm that there is a 8.2% improvement in mAP compared to the original SSD and some existing algorithms.

scholarly journals Small-Object Detection in UAV-Captured Images via Multi-Branch Parallel Feature Pyramid Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 145740-145750 ◽  
Author(s):  
Yingjie Liu ◽  
Fengbao Yang ◽  
Peng Hu
Keyword(s):  
Object Detection ◽  
Small Object ◽  
Feature Pyramid ◽  
Parallel Feature ◽  
Small Object Detection

scholarly journals Object Detection in Remote Sensing Images Based on a Scene-Contextual Feature Pyramid Network

2019 ◽  
Vol 11 (3) ◽  
pp. 339 ◽  
Author(s):  
Chaoyue Chen ◽  
Weiguo Gong ◽  
Yongliang Chen ◽  
Weihong Li
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Contextual Information ◽  
Model Performance ◽  
Small Object ◽  
Remote Sensing Images ◽  
Art Object ◽  
Contextual Feature ◽  
Feature Pyramid ◽  
Small Object Detection

Object detection has attracted increasing attention in the field of remote sensing image analysis. Complex backgrounds, vertical views, and variations in target kind and size in remote sensing images make object detection a challenging task. In this work, considering that the types of objects are often closely related to the scene in which they are located, we propose a convolutional neural network (CNN) by combining scene-contextual information for object detection. Specifically, we put forward the scene-contextual feature pyramid network (SCFPN), which aims to strengthen the relationship between the target and the scene and solve problems resulting from variations in target size. Additionally, to improve the capability of feature extraction, the network is constructed by repeating a building aggregated residual block. This block increases the receptive field, which can extract richer information for targets and achieve excellent performance with respect to small object detection. Moreover, to improve the proposed model performance, we use group normalization, which divides the channels into groups and computes the mean and variance for normalization within each group, to solve the limitation of the batch normalization. The proposed method is validated on a public and challenging dataset. The experimental results demonstrate that our proposed method outperforms other state-of-the-art object detection models.

Extended Feature Pyramid Network for Small Object Detection

2021 ◽  
pp. 1-1
Author(s):  
Chunfang Deng ◽  
Mengmeng Wang ◽  
Liang Liu ◽  
Yong Liu ◽  
Yunliang Jiang
Keyword(s):  
Object Detection ◽  
Small Object ◽  
Feature Pyramid ◽  
Small Object Detection

A Study of an X-band FMCW Radar for Small Object Detection

2021 ◽  
Author(s):  
Tripop Tongboonsong ◽  
Akkarat Boonpoonga ◽  
Kittisak Phaebua ◽  
Titipong Lertwiriyaprapa ◽  
Lakkhana Bannawat
Keyword(s):  
Object Detection ◽  
Small Object ◽  
Fmcw Radar ◽  
X Band ◽  
Small Object Detection

Realize Your Surroundings: Exploiting Context Information for Small Object Detection

2021 ◽  
Author(s):  
Jiaxu Leng ◽  
Yihui Ren ◽  
Wen Jiang ◽  
Xiaoding Sun ◽  
Ye Wang
Keyword(s):  
Object Detection ◽  
Context Information ◽  
Small Object ◽  
Small Object Detection
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