scholarly journals Vehicle Detection in Remote Sensing Image Based on Machine Vision

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Liming Zhou ◽  
Chang Zheng ◽  
Haoxin Yan ◽  
Xianyu Zuo ◽  
Baojun Qiao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Target Detection ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Small Target ◽  
Remote Sensing Images ◽  
Target Feature ◽  
Original Algorithm ◽  
Wide Range ◽  
Small Targets

Target detection in remote sensing images is very challenging research. Followed by the recent development of deep learning, the target detection algorithm has obtained large and fast growth. However, in the application of remote sensing images, due to the small target, wide range, small texture, and complex background, the existing target detection methods cannot achieve people’s hope. In this paper, a target detection algorithm named IR-PANet for remote sensing images of an automobile is proposed. In the backbone network CSPDarknet53, SPP is used to strengthen the learning content. Then, IR-PANet is used as the neck network. After the upper sampling, depthwise separable convolution is used to greatly avoid the lack of small target feature information in the convolution of the shallow network and increase the semantic information in the high-level network. Finally, Gamma correction is used to preprocess the image before image training, which effectively reduces the interference of shadow and other factors on training. The experiment proves that the method has a better effect on small targets obscured by shadows and under the color similar to the background of the picture, and the accuracy is significantly improved based on the original algorithm.

scholarly journals Research on Airplane and Ship Detection of Aerial Remote Sensing Images Based on Convolutional Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4696
Author(s):  
Changqing Cao ◽  
Jin Wu ◽  
Xiaodong Zeng ◽  
Zhejun Feng ◽  
Ting Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Target Detection ◽  
Aerial Images ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Detection Algorithms ◽  
Wide Range ◽  
Aerial Remote Sensing ◽  
Small Targets ◽  
Improved Algorithm

The wide range, complex background, and small target size of aerial remote sensing images results in the low detection accuracy of remote sensing target detection algorithms. Traditional detection algorithms have low accuracy and slow speed, making it difficult to achieve the precise positioning of small targets. This paper proposes an improved algorithm based on You Only Look Once (YOLO)-v3 for target detection of remote sensing images. Due to the difficulty in obtaining the datasets, research on small targets for complex images, such as airplanes and ships, is the focus of research. To make up for the problem of insufficient data, we screen specific types of training samples from the DOTA (Dataset of Object Detection in Aerial Images) dataset and select small targets in two different complex backgrounds of airplanes and ships to jointly evaluate the optimization degree of the improved network. We compare the improved algorithm with other state-of-the-art target detection algorithms. The results show that the performance indexes of both datasets are ameliorated by 1–3%, effectively verifying the superiority of the improved algorithm.

INFRARED SMALL TARGET DETECTION ALGORITHM BASED ON FEATURE SALIENCE AND MULTI-FEATURES FUSION

2011 ◽  
Vol 25 (02) ◽  
pp. 299-308 ◽  
Author(s):  
ZHEN-XUE CHEN ◽  
CHENG-YUN LIU ◽  
FA-LIANG CHANG
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Absolute Difference ◽  
Small Target ◽  
Probability Of Error ◽  
Small Target Detection ◽  
Low Snr ◽  
Complex Background ◽  
Small Targets

It is an important and challenging problem to detect small targets in clutter scene and low SNR (Signal Noise Ratio) in infrared (IR) images. In order to solve this problem, a method based on feature salience is proposed for automatic detection of targets in complex background. Firstly, in this paper, the method utilizes the average absolute difference maximum (AADM) as the dissimilarity measurement between targets and background region to enhance targets. Secondly, minimum probability of error was used to build the model of feature salience. Finally, by computing the realistic degree of features, this method solves the problem of multi-feather fusion. Experimental results show that the algorithm proposed shows better performance with respect to the probability of detection. It is an effective and valuable small target detection algorithm under a complex background.

scholarly journals Multi-Scale DenseNets-Based Aircraft Detection from Remote Sensing Images

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5270
Author(s):  
Yantian Wang ◽  
Haifeng Li ◽  
Peng Jia ◽  
Guo Zhang ◽  
Taoyang Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Detection Methods ◽  
Computational Time ◽  
Semantic Features ◽  
Remote Sensing Images ◽  
Bottom Level ◽  
Multi Scale ◽  
Noticeable Improvement ◽  
Small Targets ◽  
Aircraft Detection

Deep learning-based aircraft detection methods have been increasingly implemented in recent years. However, due to the multi-resolution imaging modes, aircrafts in different images show very wide diversity on size, view and other visual features, which brings great challenges to detection. Although standard deep convolution neural networks (DCNN) can extract rich semantic features, they destroy the bottom-level location information. The features of small targets may also be submerged by redundant top-level features, resulting in poor detection. To address these problems, we proposed a compact multi-scale dense convolutional neural network (MS-DenseNet) for aircraft detection in remote sensing images. Herein, DenseNet was utilized for feature extraction, which enhances the propagation and reuse of the bottom-level high-resolution features. Subsequently, we combined feature pyramid network (FPN) with DenseNet to form a MS-DenseNet for learning multi-scale features, especially features of small objects. Finally, by compressing some of the unnecessary convolution layers of each dense block, we designed three new compact architectures: MS-DenseNet-41, MS-DenseNet-65, and MS-DenseNet-77. Comparative experiments showed that the compact MS-DenseNet-65 obtained a noticeable improvement in detecting small aircrafts and achieved state-of-the-art performance with a recall of 94% and an F1-score of 92.7% and cost less computational time. Furthermore, the experimental results on robustness of UCAS-AOD and RSOD datasets also indicate the good transferability of our method.

scholarly journals Dim and Small Target Detection Based on Local Energy Aggregation Degree of Sequence Images

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Fan Xiangsuo ◽  
Xu Zhiyong
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Background Modeling ◽  
Small Target ◽  
Local Energy ◽  
Dynamic Scenes ◽  
Detection Ability ◽  
Small Target Detection ◽  
The Difference ◽  
Small Targets

In order to improve the detection ability of dim and small targets in dynamic scenes, this paper first proposes an anisotropic gradient background modeling method combined with spatial and temporal information and then uses the multidirectional gradient maximum of neighborhood blocks to segment the difference maps. On the basis of previous background modeling and segmentation extraction candidate targets, a dim small target detection algorithm for local energy aggregation degree of sequence images is proposed. Experiments show that compared with the traditional algorithm, this method can eliminate the interference of noise to the target and improve the detection ability of the system effectively.

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 PDAM–STPNNet: A Small Target Detection Approach for Wildland Fire Smoke through Remote Sensing Images

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2260
Author(s):  
Jialei Zhan ◽  
Yaowen Hu ◽  
Weiwei Cai ◽  
Guoxiong Zhou ◽  
Liujun Li
Keyword(s):  
Remote Sensing ◽  
Target Detection ◽  
Forest Fire ◽  
Spatial Domain ◽  
Small Scale ◽  
Small Target ◽  
Remote Sensing Images ◽  
Fire Smoke ◽  
Feature Pyramid ◽  
Small Forest

The target detection of smoke through remote sensing images obtained by means of unmanned aerial vehicles (UAVs) can be effective for monitoring early forest fires. However, smoke targets in UAV images are often small and difficult to detect accurately. In this paper, we use YOLOX-L as a baseline and propose a forest smoke detection network based on the parallel spatial domain attention mechanism and a small-scale transformer feature pyramid network (PDAM–STPNNet). First, to enhance the proportion of small forest fire smoke targets in the dataset, we use component stitching data enhancement to generate small forest fire smoke target images in a scaled collage. Then, to fully extract the texture features of smoke, we propose a parallel spatial domain attention mechanism (PDAM) to consider the local and global textures of smoke with symmetry. Finally, we propose a small-scale transformer feature pyramid network (STPN), which uses the transformer encoder to replace all CSP_2 blocks in turn on top of YOLOX-L’s FPN, effectively improving the model’s ability to extract small-target smoke. We validated the effectiveness of our model with recourse to a home-made dataset, the Wildfire Observers and Smoke Recognition Homepage, and the Bowfire dataset. The experiments show that our method has a better detection capability than previous methods.

scholarly journals Aircraft Detection in High Spatial Resolution Remote Sensing Images Combining Multi-Angle Features Driven and Majority Voting CNN

2021 ◽  
Vol 13 (11) ◽  
pp. 2207
Author(s):  
Fengcheng Ji ◽  
Dongping Ming ◽  
Beichen Zeng ◽  
Jiawei Yu ◽  
Yuanzhao Qing ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Target Detection ◽  
Input Image ◽  
Majority Voting ◽  
Detection Methods ◽  
Remote Sensing Images ◽  
Public And Private ◽  
Detection Model ◽  
Private Datasets

Aircraft is a means of transportation and weaponry, which is crucial for civil and military fields to detect from remote sensing images. However, detecting aircraft effectively is still a problem due to the diversity of the pose, size, and position of the aircraft and the variety of objects in the image. At present, the target detection methods based on convolutional neural networks (CNNs) lack the sufficient extraction of remote sensing image information and the post-processing of detection results, which results in a high missed detection rate and false alarm rate when facing complex and dense targets. Aiming at the above questions, we proposed a target detection model based on Faster R-CNN, which combines multi-angle features driven and majority voting strategy. Specifically, we designed a multi-angle transformation module to transform the input image to realize the multi-angle feature extraction of the targets in the image. In addition, we added a majority voting mechanism at the end of the model to deal with the results of the multi-angle feature extraction. The average precision (AP) of this method reaches 94.82% and 95.25% on the public and private datasets, respectively, which are 6.81% and 8.98% higher than that of the Faster R-CNN. The experimental results show that the method can detect aircraft effectively, obtaining better performance than mature target detection networks.

scholarly journals Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0259283
Author(s):  
Wentong Wu ◽  
Han Liu ◽  
Lingling Li ◽  
Yilin Long ◽  
Xiaodong Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Target Detection ◽  
Detection Method ◽  
Image Feature ◽  
Remote Sensing Images ◽  
Data Set ◽  
Detection Ability ◽  
Small Targets

This exploration primarily aims to jointly apply the local FCN (fully convolution neural network) and YOLO-v5 (You Only Look Once-v5) to the detection of small targets in remote sensing images. Firstly, the application effects of R-CNN (Region-Convolutional Neural Network), FRCN (Fast Region-Convolutional Neural Network), and R-FCN (Region-Based-Fully Convolutional Network) in image feature extraction are analyzed after introducing the relevant region proposal network. Secondly, YOLO-v5 algorithm is established on the basis of YOLO algorithm. Besides, the multi-scale anchor mechanism of Faster R-CNN is utilized to improve the detection ability of YOLO-v5 algorithm for small targets in the image in the process of image detection, and realize the high adaptability of YOLO-v5 algorithm to different sizes of images. Finally, the proposed detection method YOLO-v5 algorithm + R-FCN is compared with other algorithms in NWPU VHR-10 data set and Vaihingen data set. The experimental results show that the YOLO-v5 + R-FCN detection method has the optimal detection ability among many algorithms, especially for small targets in remote sensing images such as tennis courts, vehicles, and storage tanks. Moreover, the YOLO-v5 + R-FCN detection method can achieve high recall rates for different types of small targets. Furthermore, due to the deeper network architecture, the YOL v5 + R-FCN detection method has a stronger ability to extract the characteristics of image targets in the detection of remote sensing images. Meanwhile, it can achieve more accurate feature recognition and detection performance for the densely arranged target images in remote sensing images. This research can provide reference for the application of remote sensing technology in China, and promote the application of satellites for target detection tasks in related fields.

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