scholarly journals A New Method of Different Neural Network Depth and Feature Map Size on Remote Sensing Small Target Detection

2021 ◽  
Vol 24 (68) ◽  
pp. 21-32
Author(s):  
Yaming Cao ◽  
ZHEN YANG ◽  
CHEN GAO
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Target Detection ◽  
Detection Efficiency ◽  
Detection Accuracy ◽  
Small Target ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Small Target Detection

Convolutional neural networks (CNNs) have shown strong learning capabilities in computer vision tasks such as classification and detection. Especially with the introduction of excellent detection models such as YOLO (V1, V2 and V3) and Faster R-CNN, CNNs have greatly improved detection efficiency and accuracy. However, due to the special angle of view, small size, few features, and complicated background, CNNs that performs well in the ground perspective dataset, fails to reach a good detection accuracy in the remote sensing image dataset. To this end, based on the YOLO V3 model, we used feature maps of different depths as detection outputs to explore the reasons for the poor detection rate of small targets in remote sensing images by deep neural networks. We also analyzed the effect of neural network depth on small target detection, and found that the excessive deep semantic information of neural network has little effect on small target detection. Finally, the verification on the VEDAI dataset shows, that the fusion of shallow feature maps with precise location information and deep feature maps with rich semantics in the CNNs can effectively improve the accuracy of small target detection in remote sensing images.

scholarly journals Road Extraction from Unmanned Aerial Vehicle Remote Sensing Images Based on Improved Neural Networks

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4115 ◽  
Author(s):  
Yuxia Li ◽  
Bo Peng ◽  
Lei He ◽  
Kunlong Fan ◽  
Zhenxu Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Unmanned Aerial Vehicle ◽  
Computational Efficiency ◽  
Neural Nets ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Aerial Vehicle

Roads are vital components of infrastructure, the extraction of which has become a topic of significant interest in the field of remote sensing. Because deep learning has been a popular method in image processing and information extraction, researchers have paid more attention to extracting road using neural networks. This article proposes the improvement of neural networks to extract roads from Unmanned Aerial Vehicle (UAV) remote sensing images. D-Linknet was first considered for its high performance; however, the huge scale of the net reduced computational efficiency. With a focus on the low computational efficiency problem of the popular D-LinkNet, this article made some improvements: (1) Replace the initial block with a stem block. (2) Rebuild the entire network based on ResNet units with a new structure, allowing for the construction of an improved neural network D-Linknetplus. (3) Add a 1 × 1 convolution layer before DBlock to reduce the input feature maps, reducing parameters and improving computational efficiency. Add another 1 × 1 convolution layer after DBlock to recover the required number of output channels. Accordingly, another improved neural network B-D-LinknetPlus was built. Comparisons were performed between the neural nets, and the verification were made with the Massachusetts Roads Dataset. The results show improved neural networks are helpful in reducing the network size and developing the precision needed for road extraction.

scholarly journals A Small Target Detection Research Based on Dynamic Convolution Neural Network

2021 ◽  
Author(s):  
Chenshuai Bai ◽  
Kaijun Wu ◽  
Dicong Wang ◽  
Hong Li ◽  
Mingjun Yan ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Target Detection ◽  
Image Data ◽  
Remote Sensing Image ◽  
Convolution Neural Network ◽  
Small Target ◽  
Training Process ◽  
Small Target Detection

Abstract Because the detection effect of EfficientNet-YOLOv3 target detection algorithm is not very good, this paper proposes a small target detection research based on dynamic convolution neural network. Firstly, the dynamic convolutional neural network is introduced to replace the traditional convolutional neural network, which makes the algorithm model more robust; Secondly, in the training process, the optimization parameters are continuously adjusted to further strengthen the model structure; Finally, in order to prevent over fitting, the Learning Rate and Batch Size parameters are modified during the training process. remote sensing image The results of the proposed algorithm on RSOD remote sensing image data sets show that compared with the original EfficientNet-YOLOv3 algorithm, the (Average Precision, AP) value is increased by 1.93% and the (Log Average Miss Rate ,LAMR) value is reduced by 0.0500; The results of the proposed algorithm on TGRS-HRRSD remote sensing image data set show that compared with the original EfficientNet-YOLOv3 algorithm, the mAP value is increased by 0.07% and the mLAMR value is reduced by 0.0007.

Improved SSD Algorithm and Its Performance Analysis of Small Target Detection in Remote Sensing Images

2019 ◽  
Vol 39 (6) ◽  
pp. 0628005 ◽  
Author(s):  
王俊强 Junqiang Wang ◽  
李建胜 Jiansheng Li ◽  
周学文 Xuewen Zhou ◽  
张旭 Xu Zhang
Keyword(s):  
Remote Sensing ◽  
Performance Analysis ◽  
Target Detection ◽  
Small Target ◽  
Remote Sensing Images ◽  
Small Target Detection

scholarly journals Infrared small target detection based on region proposal and CNN classifier

2021 ◽  
Author(s):  
Mingming Fan ◽  
Shaoqing Tian ◽  
Kai Liu ◽  
Jiaxin Zhao ◽  
Yunsong Li
Keyword(s):  
Neural Network ◽  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Detection Rate ◽  
Corner Detection ◽  
Small Target ◽  
Small Target Detection ◽  
Potential Target ◽  
Infrared Small Target

AbstractInfrared small target detection has been a challenging task due to the weak radiation intensity of targets and the complexity of the background. Traditional methods using hand-designed features are usually effective for specific background and have the problems of low detection rate and high false alarm rate in complex infrared scene. In order to fully exploit the features of infrared image, this paper proposes an infrared small target detection method based on region proposal and convolution neural network. Firstly, the small target intensity is enhanced according to the local intensity characteristics. Then, potential target regions are proposed by corner detection to ensure high detection rate of the method. Finally, the potential target regions are fed into the classifier based on convolutional neural network to eliminate the non-target regions, which can effectively suppress the complex background clutter. Extensive experiments demonstrate that the proposed method can effectively reduce the false alarm rate, and outperform other state-of-the-art methods in terms of subjective visual impression and quantitative evaluation metrics.

scholarly journals Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 26 (1) ◽  
pp. 200-215
Author(s):  
Muhammad Alam ◽  
Jian-Feng Wang ◽  
Cong Guangpei ◽  
LV Yunrong ◽  
Yuanfang Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Natural Scene ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

scholarly journals Improved YOLO Network for Free-Angle Remote Sensing Target Detection

2021 ◽  
Vol 13 (11) ◽  
pp. 2171
Author(s):  
Yuhao Qing ◽  
Wenyi Liu ◽  
Liuyan Feng ◽  
Wanjia Gao
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Target Detection ◽  
Multiple Scales ◽  
Classification Problem ◽  
Input Image ◽  
Detection Accuracy ◽  
Feature Maps ◽  
Regression Problem ◽  
Public Datasets

Despite significant progress in object detection tasks, remote sensing image target detection is still challenging owing to complex backgrounds, large differences in target sizes, and uneven distribution of rotating objects. In this study, we consider model accuracy, inference speed, and detection of objects at any angle. We also propose a RepVGG-YOLO network using an improved RepVGG model as the backbone feature extraction network, which performs the initial feature extraction from the input image and considers network training accuracy and inference speed. We use an improved feature pyramid network (FPN) and path aggregation network (PANet) to reprocess feature output by the backbone network. The FPN and PANet module integrates feature maps of different layers, combines context information on multiple scales, accumulates multiple features, and strengthens feature information extraction. Finally, to maximize the detection accuracy of objects of all sizes, we use four target detection scales at the network output to enhance feature extraction from small remote sensing target pixels. To solve the angle problem of any object, we improved the loss function for classification using circular smooth label technology, turning the angle regression problem into a classification problem, and increasing the detection accuracy of objects at any angle. We conducted experiments on two public datasets, DOTA and HRSC2016. Our results show the proposed method performs better than previous methods.

scholarly journals Intelligent Ship Detection in Remote Sensing Images Based on Multi-Layer Convolutional Feature Fusion

2020 ◽  
Vol 12 (20) ◽  
pp. 3316 ◽  
Author(s):  
Yulian Zhang ◽  
Lihong Guo ◽  
Zengfa Wang ◽  
Yang Yu ◽  
Xinwei Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Feature Fusion ◽  
Atmospheric Correction ◽  
Recall Rate ◽  
Google Earth ◽  
Superior Performance ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Ship Detection

Intelligent detection and recognition of ships from high-resolution remote sensing images is an extraordinarily useful task in civil and military reconnaissance. It is difficult to detect ships with high precision because various disturbances are present in the sea such as clouds, mist, islands, coastlines, ripples, and so on. To solve this problem, we propose a novel ship detection network based on multi-layer convolutional feature fusion (CFF-SDN). Our ship detection network consists of three parts. Firstly, the convolutional feature extraction network is used to extract ship features of different levels. Residual connection is introduced so that the model can be designed very deeply, and it is easy to train and converge. Secondly, the proposed network fuses fine-grained features from shallow layers with semantic features from deep layers, which is beneficial for detecting ship targets with different sizes. At the same time, it is helpful to improve the localization accuracy and detection accuracy of small objects. Finally, multiple fused feature maps are used for classification and regression, which can adapt to ships of multiple scales. Since the CFF-SDN model uses a pruning strategy, the detection speed is greatly improved. In the experiment, we create a dataset for ship detection in remote sensing images (DSDR), including actual satellite images from Google Earth and aerial images from electro-optical pod. The DSDR dataset contains not only visible light images, but also infrared images. To improve the robustness to various sea scenes, images under different scales, perspectives and illumination are obtained through data augmentation or affine transformation methods. To reduce the influence of atmospheric absorption and scattering, a dark channel prior is adopted to solve atmospheric correction on the sea scenes. Moreover, soft non-maximum suppression (NMS) is introduced to increase the recall rate for densely arranged ships. In addition, better detection performance is observed in comparison with the existing models in terms of precision rate and recall rate. The experimental results show that the proposed detection model can achieve the superior performance of ship detection in optical remote sensing image.

scholarly journals Small Target Detection Algorithm Based on Transfer Learning and Deep Separable Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Peng Wang ◽  
Haiyan Wang ◽  
Xiaoyan Li ◽  
Lingling Zhang ◽  
Ruohai Di ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Target Detection ◽  
Activation Function ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Small Target ◽  
Data Set ◽  
Small Target Detection ◽  
Low Level ◽  
Half Axis

With the development of deep learning, target detection from vision sensor has achieved high accuracy and efficiency. However, small target detection remains a challenge due to inadequate use of semantic information and detailed texture information of underlying features. To solve the above problems, this paper proposes a small target detection algorithm based on Mask R-CNN model which integrates transfer learning and deep separable network. Firstly, the feature pyramid fusion structure is introduced to enhance the learning effect of low-level and high-level features, especially to strengthen the information channel of low-level feature and meanwhile optimize the feature information of small target. Secondly, the ELU function is used as the activation function to solve the problem that the original activation function disappears in the negative half axis gradient. Finally, a new loss function F-Softmax combined with Focal Loss was adopted to solve the imbalance of positive and negative sample proportions. In this paper, self-made data set is used to carry out experiments, and the experimental results show that the proposed algorithm makes the detection accuracy of small targets reach 66.5%.

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