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Ultra-deep channel sand body target recognition method based on improved deep learning under UAV cluster

  • Jingxin Guan EMAIL logo and Weimin Ma
Published/Copyright: April 4, 2024
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Open Geosciences
From the journal Open Geosciences Volume 16 Issue 1

Abstract

River sand bodies have complex and changeable characteristics and distribution. In order to improve the accuracy and efficiency of target recognition, this study proposes a target recognition method of ultra-deep river sand bodies with improved deep learning under unmanned aerial vehicle (UAV) cluster. By constructing the cooperative target allocation model of UAV group, it is ensured that the targets of ultra-deep and large-area river sand bodies are collected. The gradient histogram is used to extract the image characteristics of ultra-deep river sand body and enhance the target image of ultra-deep river sand body. Bi-directional long short-term memory (Bi-LSTM) network model is constructed by introducing bidirectional recurrent neural network (RNN) to improve deep learning. Bi-LSTM neural network is used to construct the target recognition model of ultra-deep river sand body and complete the target recognition. The experimental results show that this method can extract the target edge completely and recognize the image edge accurately, and the average recognition accuracy under different ambiguities is higher than 95. It is proved that this method has high accuracy in sand body feature extraction and classification and has great application potential in river sand body target recognition.

Keywords: Bi-LSTM; deep learning; gradient histogram; UAV cluster

Abbreviations

UAV

unmanned aerial vehicle

RNN

recurrent neural network

LSTM

long short-term memory

Bi-LSTM

bi-directional long short-term memory

1 Introduction

Due to the continuous diversion and migration of rivers, a large number of sand–mud interaction and mixed facies are developed in the fluvial facies strata, and the reservoir is thin while overlapping and crossing, which is characterized by obvious vertical and horizontal changes in lithology and weak continuity [1]. In the traditional methods of river sand target identification, sampling survey and underwater detection are usually used to collect data, and then, the target identification is carried out by manual interpretation and statistics. The application method based on artificial intelligence has the advantages of automatic feature extraction and high-precision classification and can be used in mud tunnel boring machine excavation [2], urban underground spaces management [3], building earthquake resistance [4], and shield construction [5].

Zhuang [6] completed the identification of river sand body through the sand body boundary, which solves the problem that it is difficult to identify because of the small scale and large divergence of river sand body. However, the information acquisition steps of sand body boundary are greatly influenced by the outside world, which has a deep influence on the subsequent identification results. Niu and Cheng [7] combined Wigner–Ville and the maximum entropy method to construct a distributed non-stationary signal analysis method to complete the target identification of river sand bodies, which has high time-frequency focusing and resolution. However, this method mainly relies on historical seismic data to realize channel sand body target identification, and it is difficult to process large-scale data.

In the fields of river regulation and resource exploration, unmanned aerial vehicle (UAV) cluster technology provides new possibilities for ultra-deep target recognition. Target identification of sand bodies in ultra-deep river is an important link in river regulation and resource development. However, due to the deep river and complex terrain, traditional detection methods are often difficult to meet the actual needs. UAV can quickly adjust the flying height and angle, collect high-resolution images and video data from different perspectives, and provide more comprehensive and detailed information about river sand bodies. At the same time, the UAV cluster can work together to cover a wider area of river sand bodies and improve the monitoring scope and efficiency. UAV cluster can also avoid obstacles, adapt to different environmental conditions, and transmit data in real time, which provides more stable and reliable support for river sand body target recognition. Based on this, this study proposes an improved deep learning method for target recognition of ultra-deep river sand bodies under UAV cluster. The cooperative target assignment model of UAV group is constructed, the image characteristics of ultra-deep river sand body are extracted by gradient histogram, and the target recognition is completed by bi-directional long short-term memory (Bi-LSTM) network model. The contributions of text research are as follows:

  1. Build a cooperative target allocation model of UAV group, and cover a wider range of river sand targets by reasonably allocating the navigation path and acquisition area of UAV, so as to improve the efficiency and accuracy of data collection.

  2. By calculating the gray gradient information of the image, the texture and details of the sand body can be effectively captured, and the discrimination and visualization effect of the target image can be enhanced, which lays a good foundation for the subsequent target recognition.

  3. Two-way recurrent neural network (RNN) is used to construct the target recognition model of ultra-deep river sand body, and the information before and after the current time is considered at the same time to improve the robustness of target recognition.

2 Target identification of ultra-deep channel sand body

2.1 Cooperative target assignment model of UAV group

The UAV is used to conduct a primary radar scan of ultra-deep channel sand body. When the UAV fails to meet the radar scanning work due to load failure, insufficient oil supply, and other problems [8,9,10], the ultra-deep channel sand body is carried out using a secondary scan. The state threshold of the first scan of the radar is set to α J ( 0 ≤ α J ≤ 1 ) , and the threshold of the second scan α m is selected independently by S L m according to the working condition. When the S L m scan status threshold is α m ≥ α J , only one radar scan can be performed, and the probability return of the second scan is always 0; in the case of S L m scan status threshold α m < α J , a secondary radar scan can be performed, and the probability return associated with a primary scan is always 0. The total revenue F All ( m , k ) of S L m scanning ultra-deep channel sand body image T k is set as follows:

(1) F All ( m , k ) = σ ( α m − α J ) λ 1 × F J / S ( m , k ) + λ 2 × F BRatio ( m , k ) + λ 3 × F JType ( m , k ) + ( 1 − σ ( α m − α J ) ) × λ 4 × F AR ( m , k ) ,

where λ 1 , λ 2 , λ 3 , and λ 4 are all weights, corresponding to a scanning power yield F J / S ( m , k ) , a scanning frequency band yield F BRatio ( m , k ) , a scanning style yield F JType ( m , k ) , and a secondary scanning yield F AR ( m , k ) , respectively, and 0 ≤ λ 1 , λ 2 , λ 3 , λ 4 ≤ 1 , λ 1 + λ 2 + λ 3 + λ 4 = 1 . σ ( α m − α J ) represents the step function, and the formula is as follows:

(2) σ ( α m − α J ) = 1 , α m ≥ α J , 0 , α m < α J .

Combined with the influence factors such as frequency, distance, and time of UAV clusters, the influence degree of ultra-deep channel sand body image T k is set as W k ( 0 ≤ W k ≤ 1 ) , and the constructed collaborative target allocation mathematical model is expressed as follows:

(3) max F Att = ∑ m = 1 LN ∑ k = 1 LN W k × F All ( m , k ) × x m , k ,

Constraints are as follows:

(4) ∀ x m , k ∈ { 0,1 } ( 1 ≤ m ≤ LN , 1 ≤ k ≤ TN ) ,

(5) ∑ k = 1 TN x m , k = 1 , m = 1 , 2 , ⋯ , LN ,

(6) ∑ m = 1 LN x m , k ≤ CN k , k = 1 , 2 , ⋯ , TN ,

(7) ∑ m = 1 LN x m , k ≥ CM k , k = 1 , 2 , ⋯ , TN ,

where F Att is the objective function, and the maximum benefit of UAV-cluster identification of ultra-deep channel sand body image set T can be obtained. Formula (4) represents whether S L m recognizes ultra-deep channel sand body image T k , and x m , k = 1 and x m , k = 0 describe the recognition and non-recognition, respectively. Formula (5) indicates that the same S L m can recognize only one ultra-deep channel sand image. Formula (6) indicates that the same ultra-deep channel sand body image T k can be simultaneously recognized by CN k S L m at most, and the CN k number mainly depends on factors such as the number of ultra-deep channel sand body and UAV cluster and the environment. Formula (7) indicates that T k needs to be recognized by CM k frame S L m at least simultaneously. In the case of a contradiction between formulas (6) and (7), formula (7) is preferred to ensure that all ultra-deep channel sand images can be allocated to sufficient slaves.

2.2 Feature extraction of ultra-deep channel sand body image

The gradient histogram is used to extract the features of the ultra-deep river sand body image collected by UAV. The image of ultra-deep river sand body is segmented to obtain cell units, and the characteristics of each pixel in each unit are extracted, and the gradient histograms of all blocks in the image of ultra-deep river sand body are connected in series. Grayscale the image of ultra-deep channel sand body to reduce the influence of noise on the feature extraction of ultra-deep channel sand body. The formula is

(8) I = 0.299 R + 0.587 G + 0.114 B .

Among them, the gray-level image of ultra-deep channel sand body is I ; R , G , and B indicate the red, green, and blue color channels, respectively.

Enhance I brightness and improve feature extraction effect of ultra-deep channel sand body image by gamma correction of gray-level image of ultra-deep channel sand body, and use square root gamma correction of gray-level image of ultra-deep channel sand body; the formula is as follows:

(9) I ′ ( x , y ) = I ( x , y ) C Gamma ,

The pixel point of I is ( x , y ) ; I ′ is the I that completes the gamma correction; C Gamma is the correction value. When the gamma value is less than 1, it indicates that the image contrast is reduced in the high gray scale of the image, and the gray scale of the image is increased. When the gamma value is greater than 1, the image contrast decreases and the image gray value decreases in the low gray value range.

Solve the gradient of I ′ in the horizontal and vertical directions, and obtain

(10) p x = I ′ ( x + 1 , y ) − I ′ ( x − 1 , y ) , p y = I ′ ( x , y + 1 ) − I ′ ( x , y − 1 ) ,

where p x and p y are the directional gradients; the pixel value of ( x , y ) is I ′ ( x , y ) .

Using histogram weighting ( x , y ) , the histogram features A i ( i = 1 , 2 , ⋯ , z ) in z directions of the unit are obtained.

The combined cell unit is a block, and the gradient histogram characteristics of all the cell units in the block are connected in series, namely:

(11) A = { A 1 , A 2 , ⋯ , A z } .

2.3 Identification of ultra-deep channel sand bodies based on improved deep learning

With the development of deep learning, RNN is widely used in geophysics. Although this network can solve the problem that the sequence of artificial neural network cannot be combined, because of its long dependence and gradient disappearance, long short-term memory (LSTM)network is added. Improving RNN by LSTM can effectively solve the problem of gradient disappearance in long time-series processing, but it cannot connect the time-series information of upper and lower moments. Therefore, based on two-way propagation, a Bi-LSTM network model is constructed, and all the input sequence information is used to complete the target identification of ultra-deep river sand bodies.

Bi-LSTM can learn and adjust the parameters of each layer of the network through the hidden layer and the set training rules, so that the loss function is continuously reduced and close to stability. Taking the gradient histogram characteristics of the target image of ultra-deep river sand body as input, marking the fixed river sand body label according to the image characteristics:

(12) H t = φ ( W ϑ ( A ) + V h t − 1 b ) .

Among them, the histogram of oriented gradient (HOG) feature sample of the target image of ultra-deep channel sand body is described by A , the hidden layer at t time is represented by H t , and W and V are both weight matrices. b is the bias, φ ( ⋅ ) and ϑ ( ⋅ ) are the activation and hiding functions, respectively.

When the target image of new ultra-deep channel sand body is input, the training model is used to predict the target image of new ultra-deep channel sand body, and the calculation formula of Bi-LSTM output layer is as follows:

(13) ϒ t = g ( Φ H t + b ) ,

where the output layer is ϒ t , Φ is the weight matrix, and g is the gradient.

Normalization of target image characteristics of ultra-deep channel sand body in output layer by minmax-scale method is as follows:

(14) A ′ = A − min A max A − min A ⋅ ( max − min ) + min ,

where max and min are the given upper and lower thresholds, respectively. The normalization process can effectively retain the original HOG feature relationship of super-deep channel sand body target image and solve the impact of dimension and value.

After normalizing the prediction results of ultra-deep river sand body, in order to ensure that the UAV cluster completely covers the identification area, the Bi-LSTM neural network is used to construct the target identification model of ultra-deep river sand body:

(15) R = g ( ϒ t + b ) A ′ .

The UAV that recognizes the image of ultra-deep river sand body carries out information exchange, so that the nearby UAV can continue to recognize the image of ultra-deep river sand body and complete the real-time coverage identification of ultra-deep river sand body. The overall process is shown in Figure 1.

Figure 1 Overall flow diagram.
Figure 1

Overall flow diagram.

3 Results

Take an area as an example. There are different types of rivers in this area, including rivers, lakes, and reservoirs. Water quality conditions vary from river course to river course, but overall, the water quality is relatively clear and there are few pollutants. There are not only coarse-grained sediments such as gravel and sand, but also fine-grained sediments such as clay in the rivers in this area, which form different types of sand targets in the rivers.

In the experiment, UAV was used to collect images and mark information at different time periods, from January to December 2023. The resolution of images collected by UAV is 320 × 280, and the collected images are uploaded to the cloud to build a database. Randomly select 1,000 images as training samples, 100 images as test samples, and randomly select three river sand body images from the test group as target detection samples, as shown in Figure 2.

Figure 2 River sand body sample. (a) Sample 1, (b) Sample 2, and (c) Sample 3.
Figure 2

River sand body sample. (a) Sample 1, (b) Sample 2, and (c) Sample 3.

3.1 Image edge extraction test

Ultra-deep channel sand bodies usually have irregular shapes and boundaries, and image edge extraction can effectively separate sand body targets from surrounding background. By obtaining the edge contour of the sand target, the position of the target can be accurately located. The proposed method, reference [6] method, and reference [7] method are used to extract the edge of the ultra-deep channel sand body image collected by UAV, and the results are shown in Figure 3.

Figure 3 Test results of target edge extraction by different algorithms.
Figure 3

Test results of target edge extraction by different algorithms.

As can be seen from Figure 3, when the edge of the target image is extracted, the proposed method can detect the edge of the image with good definition and no noise. However, the method of reference [6] has the situation of edge loss when extracting image edge features, and it is impossible to obtain all image edges. The method of reference [7] has the problem of poor noise suppression when extracting image edge features, and the extracted edge features are fuzzy and broken. Through this comparison, it can be seen that the edge extraction effect of the target video image is better.

3.2 Image edge recognition test

The proposed method, reference [6] method, and reference [7] method are used to identify the sand bodies in the ultra-deep river channel in the figure, and the differences of image edge identification among the three methods are obtained. The result is shown in Figure 4.

Figure 4 Test results of target edge recognition by different algorithms.
Figure 4

Test results of target edge recognition by different algorithms.

As can be seen from Figure 4, in the process of target image processing, the proposed method can accurately obtain the information of ultra-deep channel sand bodies. However, the method of reference [6] and the method of reference [7] cannot obtain the complete information of ultra-deep river sand body in the process of image edge detection of target video, and there are some deviations. This is because the proposed method applies gradient histogram feature extraction to the identification of sand bodies in ultra-deep rivers, which can better capture the texture and detail features of sand bodies. At the same time, by introducing Bi-LSTM neural network model, image features and context information can be fully integrated, and the accuracy and robustness of target recognition can be improved. Therefore, the proposed method has higher accuracy in the target identification of ultra-deep river sand bodies.

3.3 Image ambiguity test

On the basis of the aforementioned research, the recognition effect of this method is analyzed by using confusion matrix when the fuzzy degree of target images of sand bodies in ultra-deep rivers is different. The test sample images are blurred to a low degree and a high degree, respectively. In the confusion matrix, the horizontal axis is the recognition result, the vertical axis is the actual ultra-deep river sand body category, and the diagonal value is the number of images accurately recognized. The result is shown in Figure 5.

Figure 5 Recognition results of ultra-deep channel sand body images with different blurriness: (a) target image recognition effect of low fuzzy degree and ultra-deep river sand body and (b) the image recognition effect of high fuzzy ultra-deep river sand body.
Figure 5

Recognition results of ultra-deep channel sand body images with different blurriness: (a) target image recognition effect of low fuzzy degree and ultra-deep river sand body and (b) the image recognition effect of high fuzzy ultra-deep river sand body.

According to Figure 5, this method can effectively identify different categories of ultra-deep river sand bodies under different ambiguity degrees. In this study, the average recognition accuracy of the channel sand target image is 98, and the average is 95. The experiment proves that the method has high accuracy in identifying ultra-deep channel sand targets with different ambiguity.

4 Limit

Collaborative control and data transmission technology of UAV cluster is one of the key factors to realize large-area, fast, and accurate detection. At present, the cooperative control of UAV cluster mainly depends on the pre-made flight plan and local communication feedback, and its adaptability to dynamic environment and complex tasks needs to be improved. In addition, in terms of data transmission, due to the large amount of detection data of sand bodies in ultra-deep rivers, effective data compression, transmission, and storage is also a problem to be solved. The training and reasoning process of deep learning model needs to consume a lot of computing resources, including high-performance computers, a lot of training data, and long training time. This not only increases the cost of research, but also has certain limitations for real-time and portable applications. The river topography is complex and changeable, and the shapes of sand targets are different, and there may be problems such as water surface reflection, shadow, and illumination change. In addition, dynamic factors such as water flow and wind speed in the river may also affect the stable flight and target recognition of UAV clusters.

5 Conclusion

With the rapid development of science and technology, UAV technology provides a new solution for sand body target recognition in ultra-deep river. In this study, an improved deep learning method based on UAV cluster is proposed to identify sand bodies in ultra-deep rivers. This method combines UAV cluster technology and Bi-LSTM model and ensures that the targets of ultra-deep large-area river sand bodies are collected by constructing a cooperative target distribution model of UAV cluster. Gradient histogram is used to extract the features of sand targets, which enhances the recognition effect of target images. Bi-LSTM network model is constructed by introducing bidirectional RNN, which further improves the deep learning, constructs the target recognition model of ultra-deep river sand body, and realizes efficient target recognition.

In this study, an improved deep learning method for sand body target identification in ultra-deep river under UAV cluster is proposed, which provides new technical support for sand body target identification in ultra-deep river. The efficiency and accuracy of data acquisition are improved by constructing the cooperative target allocation model of UAV group. Gradient histogram method is introduced to extract features, which enhances the recognition effect of sand body target image. Bi-LSTM neural network is used to construct the target recognition model of ultra-deep river sand body, which improves the recognition accuracy of complex sand body targets.

In order to further promote the research and application of sand body target recognition in ultra-deep river, the future research can be carried out from the following aspects: first, the cooperative control and data transmission technology of UAV cluster should be deeply studied to improve the stability and real-time performance of data acquisition; second, optimize the training and reasoning algorithm of deep learning model, reduce the calculation cost, and improve the generalization ability of the model; finally, the target recognition method in complex terrain and dynamic environment is studied to improve the adaptability of the model.

  1. Funding information: The article was supported by “Guangdong Provincial Ordinary University Characteristic Innovation Project – Research on Intelligent Identification Technology for the Application of Unmanned System Cluster in the Field of River Patrol Inspection (Grant No. 2022KTSCX316),” and “Project supported by the Technology Development Center of the Ministry of Education of the People’s Republic of China – Application Technology of Unmanned System Cluster in the Field of River Patrol Inspection (Grant No. 2021ZYA12003).”

  2. Conflict of interest: The authors have no relevant financial or non-financial interests to disclose.

  3. Data availability statement: All relevant data are included in this study.

References

[1] Qin G. Washed core and tracer tests data guide the characterization of single sand body in low permeable reservoir. E3S Web Conf. 2023;375(1):01036.10.1051/e3sconf/202337501036Search in Google Scholar

[2] Qin S, Cheng Y, Zhou WH. State-of-the-art review on pressure infiltration behavior of bentonite slurry into saturated sand for TBM tunneling. Smart Constr Sustain Cities. 2023;1(1):1–14.10.1007/s44268-023-00018-ySearch in Google Scholar

[3] Peng FL, Dong YH, Wang WX, Ma CX. The next frontier: data-driven urban underground space planning orienting multiple development concepts. Smart Constr Sustain Cities. 2023;1(3):289–97.10.1007/s44268-023-00003-5Search in Google Scholar

[4] Ahmad SA, Ahmed HU, Rafiq SK, Ahmad DA. Machine learning approach for predicting compressive strength in foam concrete under varying mix designs and curing periods. Smart Constr Sustain Cities. 2023;1(16):1533–49.10.1007/s44268-023-00021-3Search in Google Scholar

[5] Yan T, Shen SL, Zhou A. GFII: A new index to identify geological features during shield tunnelling. Tunn Undergr Space Technol. 2023;142:105440.10.1016/j.tust.2023.105440Search in Google Scholar

[6] Zhuang M. The identification of the Meandering river sand boundary in Chengdao area. Geol Rev. 2019;65(S1):207–8.Search in Google Scholar

[7] Niu S, Cheng B. High-resolution channel identification method based on WVD-MEM. Oil Geophys Prospecting. 2021;56(05):1157–69 + 931–2.Search in Google Scholar

[8] Pu L, Zhang X. Deep learning based UAV vision object detection and tracking. J Beijing Univ Aeronautics Astronautics. 2022;48(5):872–80.Search in Google Scholar

[9] Chen Q, Cui H. Visual inspection model of UAV cluster based on improved pigeon flock hierarchy. J Syst Simul. 2022;34(06):1275–85.Search in Google Scholar

[10] Song J, Wang S, Liu Y, Yu Y, Chi Y. Ground small target detection algorithm of UAV based on improved FFRCNN network. Electron Opt & Control. 2022;29(7):69–73 + 80.Search in Google Scholar
Received: 2023-11-06
Revised: 2024-01-31
Accepted: 2024-02-03
Published Online: 2024-04-04

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  129. Influence of pore structure characteristics on the Permian Shan-1 reservoir in Longdong, Southwest Ordos Basin, China
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  139. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part I
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  145. Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index
  146. Analysis of the intensity of erosive processes and state of vegetation cover in the zone of influence of the Kolubara Mining Basin
  147. GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)
  148. Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies
  149. Geosite assessment as the first step for the development of canyoning activities in North Montenegro
  150. Urban geoheritage and degradation risk assessment of the Sokograd fortress (Sokobanja, Eastern Serbia)
  151. Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia
  152. Understanding seismic hazard resilience in Montenegro: A qualitative analysis of community preparedness and response capabilities
  153. Forest soil CO2 emission in Quercus robur level II monitoring site
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  155. Power of Terroir: Case study of Grašac at the Fruška Gora wine region (North Serbia)
  156. Special Issue: Geospatial and Environmental Dynamics - Part I
  157. Qualitative insights into cultural heritage protection in Serbia: Addressing legal and institutional gaps for disaster risk resilience
https://doi.org/10.1515/geo-2022-0612
Keywords for this article
Bi-LSTM; deep learning; gradient histogram; UAV cluster
Creative Commons
BY 4.0

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  148. Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies
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  151. Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia
  152. Understanding seismic hazard resilience in Montenegro: A qualitative analysis of community preparedness and response capabilities
  153. Forest soil CO2 emission in Quercus robur level II monitoring site
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  155. Power of Terroir: Case study of Grašac at the Fruška Gora wine region (North Serbia)
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