Target Detection-Based Tree Recognition in a Spruce Forest Area with a High Tree Density—Implications for Estimating Tree Numbers

Here, unmanned aerial vehicle (UAV) remote sensing and machine vision were used to automatically, accurately, and efficiently count Tianshan spruce and improve the efficiency of scientific forest management, focusing on a typical Tianshan spruce forest on Tianshan Mountain, middle Asia. First, the UAV in the sampling area was cropped from the image, and a target-labeling tool was used. The Tianshan spruce trees were annotated to construct a data set, and four models were used to identify and verify them in three different areas (low, medium, and high canopy closures). Finally, the combined number of trees was calculated. The average accuracy of the detection frame, mean accuracy and precision (mAP), was used to determine the target detection accuracy. The Faster Region Convolutional Neural Network (Faster-RCNN) model achieved the highest accuracies (96.36%, 96.32%, and 95.54% under low, medium, and high canopy closures, respectively) and the highest mAP (85%). Canopy closure affected the detection and recognition accuracy; YOLOv3, YOLOv4, and Faster-RCNN all showed varying spruce recognition accuracies at different densities. The accuracy of the Faster-RCNN model decreased by at least 0.82%. Combining UAV remote sensing with target detection networks can identify and quantify statistics regarding Tianshan spruce. This solves the shortcomings of traditional monitoring methods and is significant for understanding and monitoring forest ecosystems.

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A Target Detection Algorithm for Remote Sensing Images Based on Deep Learning

Contrast Media & Molecular Imaging ◽

10.1155/2021/3474921 ◽

2021 ◽

Vol 2021 ◽

pp. 1-6

Author(s):

Yi Lv ◽

Zhengbo Yin ◽

Zhezhou Yu

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Target Detection ◽

Detection Efficiency ◽

False Positive Rate ◽

Remote Sensing Image ◽

Detection Algorithm ◽

Detection Accuracy ◽

Data Set ◽

Detection Effect

In order to improve the accuracy of remote sensing image target detection, this paper proposes a remote sensing image target detection algorithm DFS based on deep learning. Firstly, dimension clustering module, loss function, and sliding window segmentation detection are designed. The data set used in the experiment comes from GoogleEarth, and there are 6 types of objects: airplanes, boats, warehouses, large ships, bridges, and ports. Training set, verification set, and test set contain 73490 images, 22722 images, and 2138 images, respectively. It is assumed that the number of detected positive samples and negative samples is A and B, respectively, and the number of undetected positive samples and negative samples is C and D, respectively. The experimental results show that the precision-recall curve of DFS for six types of targets shows that DFS has the best detection effect for bridges and the worst detection effect for boats. The main reason is that the size of the bridge is relatively large, and it is clearly distinguished from the background in the image, so the detection difficulty is low. However, the target of the boat is very small, and it is easy to be mixed with the background, so it is difficult to detect. The MAP of DFS is improved by 12.82%, the detection accuracy is improved by 13%, and the recall rate is slightly decreased by 1% compared with YOLOv2. According to the number of detection targets, the number of false positives (FPs) of DFS is much less than that of YOLOv2. The false positive rate is greatly reduced. In addition, the average IOU of DFS is 11.84% higher than that of YOLOv2. For small target detection efficiency and large remote sensing image detection, the DFS algorithm has obvious advantages.

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Implementation of a Modified Faster R-CNN for Target Detection Technology of Coastal Defense Radar

Remote Sensing ◽

10.3390/rs13091703 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1703

Author(s):

He Yan ◽

Chao Chen ◽

Guodong Jin ◽

Jindong Zhang ◽

Xudong Wang ◽

...

Keyword(s):

False Alarm ◽

False Alarm Rate ◽

Target Detection ◽

Real Data ◽

Detection Performance ◽

Detection Accuracy ◽

Constant False Alarm Rate ◽

Data Set ◽

Detection Technology ◽

Coastal Defense

The traditional method of constant false-alarm rate detection is based on the assumption of an echo statistical model. The target recognition accuracy rate and the high false-alarm rate under the background of sea clutter and other interferences are very low. Therefore, computer vision technology is widely discussed to improve the detection performance. However, the majority of studies have focused on the synthetic aperture radar because of its high resolution. For the defense radar, the detection performance is not satisfactory because of its low resolution. To this end, we herein propose a novel target detection method for the coastal defense radar based on faster region-based convolutional neural network (Faster R-CNN). The main processing steps are as follows: (1) the Faster R-CNN is selected as the sea-surface target detector because of its high target detection accuracy; (2) a modified Faster R-CNN based on the characteristics of sparsity and small target size in the data set is employed; and (3) soft non-maximum suppression is exploited to eliminate the possible overlapped detection boxes. Furthermore, detailed comparative experiments based on a real data set of coastal defense radar are performed. The mean average precision of the proposed method is improved by 10.86% compared with that of the original Faster R-CNN.

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Improved YOLO Network for Free-Angle Remote Sensing Target Detection

Remote Sensing ◽

10.3390/rs13112171 ◽

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.

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

Journal of Sensors ◽

10.1155/2021/9006288 ◽

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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Research on Defect Detection of Electric Energy Metering Box Based on YOLOv5

Journal of Physics Conference Series ◽

10.1088/1742-6596/2087/1/012081 ◽

2021 ◽

Vol 2087 (1) ◽

pp. 012081

Author(s):

Yong Yu ◽

Yanchao Sun ◽

Chunxue Zhao ◽

Chong Qu

Keyword(s):

Data Storage ◽

Electric Energy ◽

Detection Accuracy ◽

Damage State ◽

Data Set ◽

Operation And Maintenance ◽

Average Accuracy ◽

Energy Metering ◽

Inspection Work ◽

Power Metering

Abstract The manual inspection for the damage state of the electric energy metering box consumes a lot of time, the workload is large, and the data storage is difficult. In order to solve these problems, this paper proposes an automatic detection method for the damage state of the electric energy metering box based on the YOLOv5 algorithm. The actual metering box pictures taken by the operation and maintenance inspectors are used as the training set, LabelImage is used to annotate the data set, and YOLOv5s model is used to train the data set. The experimental results show that the method proposed in this paper can accurately mark the position of the metering box lid and accurately predict its damage state. The average accuracy reaches 98%, which can meet the requirements for the detection accuracy of the power metering box damage state in the operation and maintenance inspection work.

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Parallel Ensemble Deep Learning for Real-Time Remote Sensing Video Multi-Target Detection

Remote Sensing ◽

10.3390/rs13214377 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4377

Author(s):

Long Sun ◽

Jie Chen ◽

Dazheng Feng ◽

Mengdao Xing

Keyword(s):

Deep Learning ◽

Real Time ◽

Unmanned Aerial Vehicle ◽

Target Detection ◽

Template Matching ◽

Detection Algorithm ◽

Detection Accuracy ◽

Information Warfare ◽

Aerial Vehicle ◽

Uav Images

Unmanned aerial vehicle (UAV) is one of the main means of information warfare, such as in battlefield cruises, reconnaissance, and military strikes. Rapid detection and accurate recognition of key targets in UAV images are the basis of subsequent military tasks. The UAV image has characteristics of high resolution and small target size, and in practical application, the detection speed is often required to be fast. Existing algorithms are not able to achieve an effective trade-off between detection accuracy and speed. Therefore, this paper proposes a parallel ensemble deep learning framework for unmanned aerial vehicle video multi-target detection, which is a global and local joint detection strategy. It combines a deep learning target detection algorithm with template matching to make full use of image information. It also integrates multi-process and multi-threading mechanisms to speed up processing. Experiments show that the system has high detection accuracy for targets with focal lengths varying from one to ten times. At the same time, the real-time and stable display of detection results is realized by aiming at the moving UAV video image.

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

INTELIGENCIA ARTIFICIAL ◽

10.4114/intartif.vol24iss68pp21-32 ◽

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.

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An improved YOLO v3 algorithm for remote Sensing image target detection

Journal of Physics Conference Series ◽

10.1088/1742-6596/2132/1/012028 ◽

2021 ◽

Vol 2132 (1) ◽

pp. 012028

Author(s):

Fan Yang

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Target Detection ◽

Remote Sensing Data ◽

Remote Sensing Image ◽

Utilization Efficiency ◽

Width Ratio ◽

False Detection ◽

Data Set ◽

Original Algorithm

Abstract With more and more in-depth research on deep learning algorithms in recent years, how to use deep learning method to detect remote sensing images is the key to improving the utilization efficiency of remote sensing data and realizing the transformation from data to knowledge. In this paper, an improved YOLO V3 algorithm is proposed to solve the problems of missed detection and false detection of the original YOLOv3 algorithm in remote sensing image target detection with different size and wide disparity in length and width ratio. first of all, K-means algorithm is used for clustering analysis of data set to obtain the position of anchor box; Secondly, the dilated convolution with expansion rate of 2 is used to replace the general convolution in the feature extraction part; Then four scales are used for prediction; Finally, the improved algorithm is applied to the recognition of bridges, harbors and airports. The results show that the detection performance of the algorithm is improved by about 2% compared with the original algorithm.

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Automatic Extraction of Damaged Houses by Earthquake Based on Improved YOLOv5: A Case Study in Yangbi

Remote Sensing ◽

10.3390/rs14020382 ◽

2022 ◽

Vol 14 (2) ◽

pp. 382

Author(s):

Yafei Jing ◽

Yuhuan Ren ◽

Yalan Liu ◽

Dacheng Wang ◽

Linjun Yu

Keyword(s):

Remote Sensing ◽

Feature Fusion ◽

Flying Height ◽

Multi Scale ◽

Average Accuracy ◽

Proposed Model ◽

High Intelligence ◽

Aerial Vehicle ◽

Feature Pyramid ◽

Uav Images

Efficiently and automatically acquiring information on earthquake damage through remote sensing has posed great challenges because the classical methods of detecting houses damaged by destructive earthquakes are often both time consuming and low in accuracy. A series of deep-learning-based techniques have been developed and recent studies have demonstrated their high intelligence for automatic target extraction for natural and remote sensing images. For the detection of small artificial targets, current studies show that You Only Look Once (YOLO) has a good performance in aerial and Unmanned Aerial Vehicle (UAV) images. However, less work has been conducted on the extraction of damaged houses. In this study, we propose a YOLOv5s-ViT-BiFPN-based neural network for the detection of rural houses. Specifically, to enhance the feature information of damaged houses from the global information of the feature map, we introduce the Vision Transformer into the feature extraction network. Furthermore, regarding the scale differences for damaged houses in UAV images due to the changes in flying height, we apply the Bi-Directional Feature Pyramid Network (BiFPN) for multi-scale feature fusion to aggregate features with different resolutions and test the model. We took the 2021 Yangbi earthquake with a surface wave magnitude (Ms) of 6.4 in Yunan, China, as an example; the results show that the proposed model presents a better performance, with the average precision (AP) being increased by 9.31% and 1.23% compared to YOLOv3 and YOLOv5s, respectively, and a detection speed of 80 FPS, which is 2.96 times faster than YOLOv3. In addition, the transferability test for five other areas showed that the average accuracy was 91.23% and the total processing time was 4 min, while 100 min were needed for professional visual interpreters. The experimental results demonstrate that the YOLOv5s-ViT-BiFPN model can automatically detect damaged rural houses due to destructive earthquakes in UAV images with a good performance in terms of accuracy and timeliness, as well as being robust and transferable.

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

PLoS ONE ◽

10.1371/journal.pone.0259283 ◽

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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