scholarly journals Dilated Convolution and Feature Fusion SSD Network for Small Object Detection in Remote Sensing Images

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 82832-82843 ◽  
Author(s):  
Junsuo Qu ◽  
Chang Su ◽  
Zhiwei Zhang ◽  
Abolfazl Razi
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Feature Fusion ◽  
Small Object ◽  
Remote Sensing Images ◽  
Dilated Convolution ◽  
Small Object Detection

scholarly journals Small Object Detection in Remote Sensing Images Based on Super-Resolution with Auxiliary Generative Adversarial Networks

2020 ◽  
Vol 12 (19) ◽  
pp. 3152
Author(s):  
Luc Courtrai ◽  
Minh-Tan Pham ◽  
Sébastien Lefèvre
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Network Architecture ◽  
Super Resolution ◽  
Remote Sensing Data ◽  
Generative Adversarial Networks ◽  
Small Object ◽  
Remote Sensing Images ◽  
Generative Adversarial Network ◽  
Small Object Detection

This article tackles the problem of detecting small objects in satellite or aerial remote sensing images by relying on super-resolution to increase image spatial resolution, thus the size and details of objects to be detected. We show how to improve the super-resolution framework starting from the learning of a generative adversarial network (GAN) based on residual blocks and then its integration into a cycle model. Furthermore, by adding to the framework an auxiliary network tailored for object detection, we considerably improve the learning and the quality of our final super-resolution architecture, and more importantly increase the object detection performance. Besides the improvement dedicated to the network architecture, we also focus on the training of super-resolution on target objects, leading to an object-focused approach. Furthermore, the proposed strategies do not depend on the choice of a baseline super-resolution framework, hence could be adopted for current and future state-of-the-art models. Our experimental study on small vehicle detection in remote sensing data conducted on both aerial and satellite images (i.e., ISPRS Potsdam and xView datasets) confirms the effectiveness of the improved super-resolution methods to assist with the small object detection tasks.

scholarly journals SSD-TSEFFM: New SSD Using Trident Feature and Squeeze and Extraction Feature Fusion

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3630 ◽  
Author(s):  
Young-Joon Hwang ◽  
Jin-Gu Lee ◽  
Un-Chul Moon ◽  
Ho-Hyun Park
Keyword(s):  
Object Detection ◽  
Semantic Information ◽  
Feature Fusion ◽  
Contextual Information ◽  
Single Shot ◽  
Small Object ◽  
Dilated Convolution ◽  
Average Improvement ◽  
Proposed Model ◽  
Small Object Detection

The single shot multi-box detector (SSD) exhibits low accuracy in small-object detection; this is because it does not consider the scale contextual information between its layers, and the shallow layers lack adequate semantic information. To improve the accuracy of the original SSD, this paper proposes a new single shot multi-box detector using trident feature and squeeze and extraction feature fusion (SSD-TSEFFM); this detector employs the trident network and the squeeze and excitation feature fusion module. Furthermore, a trident feature module (TFM) is developed, inspired by the trident network, to consider the scale contextual information. The use of this module makes the proposed model robust to scale changes owing to the application of dilated convolution. Further, the squeeze and excitation block feature fusion module (SEFFM) is used to provide more semantic information to the model. The SSD-TSEFFM is compared with the faster regions with convolution neural network features (RCNN) (2015), SSD (2016), and DF-SSD (2020) on the PASCAL VOC 2007 and 2012 datasets. The experimental results demonstrate the high accuracy of the proposed model in small-object detection, in addition to a good overall accuracy. The SSD-TSEFFM achieved 80.4% mAP and 80.2% mAP on the 2007 and 2012 datasets, respectively. This indicates an average improvement of approximately 2% over other models.

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.

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