A Deep Lightweight Convolutional Neural Network Method for Real-Time Small Object Detection in Optical Remote Sensing Images

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yanyong Han ◽  
Yandong Han
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Optical Remote Sensing ◽  
Small Object ◽  
Remote Sensing Images ◽  
Network Method ◽  
Small Object Detection

Rotation-aware and multi-scale convolutional neural network for object detection in remote sensing images

2020 ◽  
Vol 161 ◽  
pp. 294-308 ◽  
Author(s):  
Kun Fu ◽  
Zhonghan Chang ◽  
Yue Zhang ◽  
Guangluan Xu ◽  
Keshu Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Remote Sensing Images ◽  
Multi Scale

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 Effective Complex Airport Object Detection in Remote Sensing Images Based on Improved End-to-End Convolutional Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 172652-172663
Author(s):  
Yongsai Han ◽  
Shiping Ma ◽  
Yuelei Xu ◽  
Linyuan He ◽  
Shuai Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Remote Sensing Images ◽  
End To End

scholarly journals A robust approach for industrial small-object detection using an improved faster regional convolutional neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Faisal Saeed ◽  
Muhammad Jamal Ahmed ◽  
Malik Junaid Gul ◽  
Kim Jeong Hong ◽  
Anand Paul ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Industrial Sector ◽  
Small Object ◽  
Industrial Products ◽  
Amplification Method ◽  
Small Object Detection ◽  
Improved Model

AbstractWith the increasing pace in the industrial sector, the need for a smart environment is also increasing and the production of industrial products in terms of quality always matters. There is a strong burden on the industrial environment to continue to reduce impulsive downtime, concert deprivation, and safety risks, which needs an efficient solution to detect and improve potential obligations as soon as possible. The systems working in industrial environments for generating industrial products are very fast and generate products rapidly, sometimes leading to faulty products. Therefore, this problem needs to be solved efficiently. Considering this problem in terms of faulty small-object detection, this study proposed an improved faster regional convolutional neural network-based model to detect the faults in the product images. We introduced a novel data-augmentation method along with a bi-cubic interpolation-based feature amplification method. A center loss is also introduced in the loss function to decrease the inter-class similarity issue. The experimental results show that the proposed improved model achieved better classification accuracy for detecting our small faulty objects. The proposed model performs better than the state-of-the-art methods.

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