CN117671504A - An offshore wind power identification method and system based on yolo algorithm - Google Patents

Abstract

The invention discloses an offshore wind power identification method and system based on the yolo algorithm, and relates to the field of machine learning technology. The method includes: collecting remote sensing images of offshore wind farms in multiple areas and producing an offshore wind power sample set; building an initial offshore wind power identification model based on the YOLOv5 algorithm, CA attention mechanism and RFB multi-branch convolution module; using the offshore wind power sample set to The initial offshore wind power recognition model is trained and verified to obtain the target offshore wind power recognition model; real-time offshore wind film images are obtained, and the target offshore wind power recognition model is used to identify offshore wind power targets. The invention can accurately and stably detect offshore wind power, effectively improving detection efficiency and detection accuracy.

Description

An offshore wind power identification method and system based on yolo algorithm

Technical field

The present invention relates to the field of machine learning technology, and specifically to an offshore wind power identification method and system based on the yolo algorithm.

Background technique

Existing deep learning detection algorithms are mainly based on R-CNN (Girshick, 2015; Girshick et al., 2014; He et al., 2017; Ren et al., 2017), Fast R-CNN (Girshick, 2015), Two-level target detection networks represented by Faster R-CNN (Ren et al., 2017), SPP-NET (He et al., 2014), and YOLO (Bochkovskiy et al., 2020; Redmon et al., 2016 ; Redmon and Farhadi, 2018, 2017) and SSD (W. Liu et al., 2016) are representative first-level target detection networks. The two-level network has high detection accuracy, but requires a large number of data sets, high training costs, and the detection speed is not fast. The one-stage detection method directly extracts features from the network and predicts the target category and location. It has fast detection speed, requires a small amount of data, and is suitable for small sample data sets.

At present, deep learning technology has made preliminary progress in the application of offshore wind power target recognition. Hoeser et al. (2022) (Hoeser and Kuenzer, 2022) constructed a global offshore wind power deep learning data set, providing a research basis for offshore target detection. Hoeser et al. (2022) (Hoeser et al., 2022) used this data set combined with the CNN model to detect global offshore wind power, and obtained the spatiotemporal distribution data set of global offshore wind power from 2016 to 2021. However, only using offshore wind power in the North Sea (Europe) and the East China Sea (China) as a verification set is not enough to evaluate the identification of offshore wind power in different sea conditions and different underlying surfaces around the world, and the experiment still has room for improvement in aspects such as lightweight deployment. Even though deep neural networks have made progress in the field of offshore wind power detection, methods based on deep learning still face three challenges: data set availability, model generalization, and robustness of detection results. First, deep learning usually requires a large amount of sample data to train the model to obtain high regional accuracy; however, there is still a lack of high-quality training data sets available in open source. Secondly, offshore wind power is sparsely distributed in the sea area, and there are temporal and spatial differences in the sea surface clutter background in remote sensing images. These factors bring huge challenges to the generalization of deep learning models. Furthermore, the target area of offshore wind power is small, and interference from floating objects on the sea or temporarily moving objects (such as ships, clouds, and oil drilling platforms) makes the wind power detection algorithm time-consuming and lacks robustness.

Therefore, how to accurately and efficiently identify national-scale offshore wind power targets has become an urgent problem that needs to be solved.

Contents of the invention

In order to overcome the above problems or at least partially solve the above problems, the present invention provides an offshore wind power identification method and system based on the yolo algorithm, which can accurately and stably detect offshore wind power, effectively improving detection efficiency and detection accuracy.

In order to solve the above technical problems, the technical solutions adopted by the present invention are:

In a first aspect, the present invention provides an offshore wind power identification method based on the yolo algorithm, which includes the following steps:

Collect remote sensing images of offshore wind farms in multiple areas and create an offshore wind power sample collection;

Build an initial offshore wind power identification model based on the YOLOv5 algorithm, CA attention mechanism and RFB multi-branch convolution module;

Use the offshore wind power sample set to train and verify the initial offshore wind power identification model to obtain the target offshore wind power identification model;

Obtain real-time offshore wind images and use the target offshore wind power identification model to identify offshore wind power targets.

This invention creates a deep learning training data set suitable for China's offshore wind power target recognition, optimizes and improves it based on the YOLOv5 algorithm in the YOLO algorithm, introduces the CA attention mechanism and RFB multi-branch convolution module, and constructs effective offshore wind power recognition Model (YOLOv5s-CR model), the coordinate attention CA module is added to improve the positioning ability of the model, and the context enhancement module RFB is integrated to increase the receptive field and reduce the error detection rate of the model in the large-scale complex sea surface background, which can realize the rapid development of China's offshore wind power detection and precise positioning. Based on the constructed offshore wind power identification model, offshore wind power is accurately and stably detected, effectively improving detection efficiency and detection accuracy. The offshore wind power identification model constructed by the present invention has good temporal generalization and geographical generalization performance.

Based on the first aspect, further, the above-mentioned method of producing an offshore wind power sample set includes the following steps:

Preprocess remote sensing images of offshore wind farms;

Perform mean calculation on the preprocessed offshore wind farm remote sensing images and optimize the offshore wind farm remote sensing images;

Perform sample labeling on the optimized offshore wind farm remote sensing images to obtain an offshore wind power sample test set and an offshore wind power sample verification set;

Use the ArcGIS Pro image processing tool to create sample slices of the offshore wind power sample test set and offshore wind power sample verification set, and write the location information and category information of all samples into the corresponding xml files;

Select some sample slice data as the training set and test set to form a data set in VOC format.

Based on the first aspect, further, the above-mentioned method for preprocessing offshore wind farm remote sensing images includes the following steps:

Update the orbit state vector of offshore wind farm remote sensing images, and use the corrected orbit file to update orbit metadata for the ground-distance multi-view image GRD in offshore wind farm remote sensing images;

Suppress low-intensity noise and invalid data at the edge of the GRD scene by masking low-value pixels to eliminate additional noise in sub-strips of offshore wind farm remote sensing images;

The sensor calibration parameters in the metadata are used to calculate the backscattering intensity for radiometric calibration, and the digital elevation model is used to perform orthorectification of remote sensing images of offshore wind farms.

Based on the first aspect, further, the offshore wind power identification method based on the yolo algorithm further includes the following steps:

Image enhancement is performed on the images in the dataset.

Based on the first aspect, further, the above-mentioned method of training and verifying the initial offshore wind power identification model using the offshore wind power sample set includes the following steps:

The initial offshore wind power identification model is trained and verified using the training set and the test set respectively.

Based on the first aspect, further, the above-mentioned method of constructing an initial offshore wind power identification model based on the YOLOv5 algorithm, CA attention mechanism and RFB multi-branch convolution module includes the following steps:

Introduce the CA attention mechanism into the Backbone module of the YOLOv5 algorithm and build the CS-CA module;

Introducing the RFB multi-branch convolution module into the backbone network of the YOLOv5 algorithm;

An initial offshore wind power identification model is constructed based on the YOLOv5 algorithm, CS-CA module and RFB multi-branch convolution module.

In the second aspect, the present invention provides an offshore wind power identification system based on the yolo algorithm, including a sample set production module, an initial model construction module, a model training and verification module, and an offshore wind power identification module, wherein:

The sample set production module is used to collect remote sensing images of offshore wind farms in multiple regions and create offshore wind power sample sets;

Initial model building module, used to build an initial offshore wind power identification model based on the YOLOv5 algorithm, CA attention mechanism and RFB multi-branch convolution module;

The model training and verification module is used to train and verify the initial offshore wind power identification model using the offshore wind power sample set to obtain the target offshore wind power identification model;

The offshore wind power identification module is used to obtain real-time offshore wind power images and use the target offshore wind power identification model to identify offshore wind power targets.

This system uses multiple modules such as the sample set production module, initial model construction module, model training and verification module, and offshore wind power identification module to build an offshore wind power identification model to accurately and stably detect offshore wind power, effectively improving detection efficiency and detection Accuracy. This invention creates a deep learning training data set suitable for China's offshore wind power target recognition, optimizes and improves it based on the YOLOv5 algorithm in the YOLO algorithm, introduces the CA attention mechanism and RFB multi-branch convolution module, and constructs effective offshore wind power recognition Model (YOLOv5s-CR model), adding the coordinate attention CA module to improve the positioning ability of the model, integrating the context enhancement module RFB to increase the receptive field and reduce the error detection rate of the model in the large-scale complex sea surface background, can achieve rapid development of China's offshore wind power detection and precise positioning. Based on the constructed offshore wind power identification model, offshore wind power is accurately and stably detected, effectively improving detection efficiency and detection accuracy. The offshore wind power identification model constructed by the present invention has good temporal generalization and geographical generalization performance.

In a third aspect, the present application provides an electronic device, which includes a memory for storing one or more programs; a processor; when one or more programs are executed by the processor, any one of the above first aspects is implemented. Methods.

In a fourth aspect, the present application provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the method in any one of the above-mentioned first aspects is implemented.

The present invention has at least the following advantages or beneficial effects:

1. Created a deep learning training data set suitable for China's offshore wind power target recognition to provide an accurate and comprehensive data set for subsequent use.

2. Based on the YOLOv5 algorithm in the YOLO algorithm, optimize and improve it, introduce the CA attention mechanism and RFB multi-branch convolution module, build an effective offshore wind power identification model (YOLOv5s-CR model), and add the coordinate attention CA module to improve the positioning of the model Ability to integrate the context enhancement module RFB to increase the receptive field and reduce the error detection rate of the model in the large-scale complex sea surface background, which can achieve rapid detection and precise positioning of China's offshore wind power. Based on the constructed offshore wind power identification model, offshore wind power is accurately and stably detected, effectively improving detection efficiency and detection accuracy.

3. The offshore wind power identification model constructed by the present invention has good temporal generalization and geographical generalization performance.

Description of drawings

In order to explain the technical solutions of the embodiments of the present invention more clearly, the drawings required to be used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and therefore do not It should be regarded as a limitation of the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without exerting creative efforts.

Figure 1 is a flow chart of an offshore wind power identification method based on the yolo algorithm according to an embodiment of the present invention;

Figure 2 is a schematic diagram of the structure and flow of the YOLOv5s-CR model in the embodiment of the present invention;

Figure 3 is a schematic diagram of the detection results of the timing experiment of a typical offshore wind farm on tidal flats in a certain area in the embodiment of the present invention;

Figure 4 is a schematic diagram of the detection results of typical offshore wind farms in other countries around the world in the embodiment of the present invention;

Figure 5 is a schematic diagram of China's offshore wind power research area and sample labeling in the embodiment of the present invention;

Figure 6 is a functional block diagram of an offshore wind power identification system based on the yolo algorithm according to an embodiment of the present invention;

Figure 7 is a structural block diagram of an electronic device provided by an embodiment of the present invention.

Explanation of reference signs: 100. Sample set production module; 200. Initial model building module; 300. Model training and verification module; 400. Offshore wind power identification module; 101. Memory; 102. Processor; 103. Communication interface.

Detailed ways

In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments These are some embodiments of the present invention, rather than all embodiments. The components of the embodiments of the invention generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations.

Therefore, the following detailed description of the embodiments of the invention provided in the appended drawings is not intended to limit the scope of the claimed invention, but rather to represent selected embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts fall within the scope of protection of the present invention.

It should be noted that similar reference numerals and letters represent similar items in the following figures, therefore, once an item is defined in one figure, it does not need further definition and explanation in subsequent figures.

It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that these entities or operations are mutually exclusive. any such actual relationship or sequence exists between them. Furthermore, the term "comprising" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article or apparatus including a list of elements includes not only those elements but also other elements not expressly listed, Or it also includes elements inherent to the process, method, article or equipment. Without further limitation, an element defined by the statement "comprises a..." does not exclude the presence of additional identical elements in a process, method, article, or apparatus that includes the stated element.

In the description of the embodiments of the present invention, “plurality” represents at least two.

Example:

As shown in Figures 1-5, in the first aspect, embodiments of the present invention provide an offshore wind power identification method based on the yolo algorithm, which includes the following steps:

S1. Collect remote sensing images of offshore wind farms in multiple areas and create an offshore wind power sample set;

S2. Build an initial offshore wind power identification model based on the YOLOv5 algorithm, CA attention mechanism and RFB multi-branch convolution module;

S3. Use the offshore wind power sample set to train and verify the initial offshore wind power identification model to obtain the target offshore wind power identification model;

S4. Obtain real-time offshore wind film images, and use the target offshore wind power identification model to identify offshore wind power targets.

This invention creates a deep learning training data set suitable for China's offshore wind power target recognition, optimizes and improves it based on the YOLOv5 algorithm in the YOLO algorithm, introduces the CA attention mechanism and RFB multi-branch convolution module, and constructs effective offshore wind power recognition Model (YOLOv5s-CR model), the coordinate attention CA module is added to improve the positioning ability of the model, and the context enhancement module RFB is integrated to increase the receptive field and reduce the error detection rate of the model in the large-scale complex sea surface background, which can realize the rapid development of China's offshore wind power detection and precise positioning. Based on the constructed offshore wind power identification model, offshore wind power is accurately and stably detected, effectively improving detection efficiency and detection accuracy. The offshore wind power identification model constructed by the present invention has good temporal generalization and geographical generalization performance.

Based on the first aspect, further, the above-mentioned method of producing an offshore wind power sample set includes the following steps:

Preprocess remote sensing images of offshore wind farms;

Perform mean calculation on the preprocessed offshore wind farm remote sensing images and optimize the offshore wind farm remote sensing images;

Perform sample labeling on the optimized offshore wind farm remote sensing images to obtain an offshore wind power sample test set and an offshore wind power sample verification set;

Use the ArcGIS Pro image processing tool to create sample slices of the offshore wind power sample test set and offshore wind power sample verification set, and write the location information and category information of all samples into the corresponding xml files;

Select some sample slice data as the training set and test set to form a data set in VOC format.

Based on the first aspect, further, the above-mentioned method for preprocessing offshore wind farm remote sensing images includes the following steps:

Update the orbit state vector of offshore wind farm remote sensing images, and use the corrected orbit file to update orbit metadata for the ground-distance multi-view image GRD in offshore wind farm remote sensing images;

Suppress low-intensity noise and invalid data at the edge of the GRD scene by masking low-value pixels to eliminate additional noise in sub-strips of offshore wind farm remote sensing images;

The sensor calibration parameters in the metadata are used to calculate the backscattering intensity for radiometric calibration, and the digital elevation model is used to perform orthorectification of remote sensing images of offshore wind farms.

In some embodiments of the present invention, offshore wind power, due to its unique three-blade rigid polyhedral shape, appears as a high-brightness spindle-shaped or oblong bright spot on high-resolution SAR remote sensing images. The contrast with the weakly backscattered sea surface is obvious in the image. Therefore, this article uses the Sentinel-1SARGRD product with a C-band and a spatial resolution of 10 meters, and selects the interference wide (IW) strip mode and the vertical-vertical (VV) polarization mode. Products are available through the Google Earth Engine (GEE) platform. The Sentinel-1 satellite consists of two polar-orbiting satellites A and B. The dual-star cooperative working mode shortens the revisit period of Sentinel-1 from 12 days to 6 days, and can provide all-weather observations. The actual sample production process is as follows. Step1: Sentinel-1 image preprocessing. Including updating the orbit state vector of SAR images, using the corrected orbit file to update orbit metadata for ground-range multi-view images (GRD); suppressing low-intensity noise and invalid data at the edge of the GRD scene by masking low-value pixels; Eliminate additional noise in sub-strips to reduce discontinuities between different sub-bands in multi-strip acquisition mode; use sensor calibration parameters in metadata to calculate backscatter intensity for radiometric calibration, combined with digital elevation model ( DEM) data is used to orthorectify the image to eliminate the influence of terrain. Step2: Calculate and download the average of the SAR images in the study area in December each year from 2015 to 2022. The aspect ratio of image slices remains consistent and is no less than 2048 pixels. Step 3: Manually label Chinese offshore wind power in the sample area (Figure 3(b)-(d)), and obtain 2143 offshore wind power samples (test set) and 744 offshore wind power samples (validation set) respectively. Step 4: Use the ArcGISPro image processing tool to create 256×256 pixel sample slices, and write the sample's location information and category information into the corresponding xml file. Step5: Then use the output 2991 sample slice data in 2022 as the training set and the 771 slice data in 2021 as the test set to form a VOC format data set.

Based on the first aspect, further, the offshore wind power identification method based on the yolo algorithm further includes the following steps:

Image enhancement is performed on the images in the dataset.

In order to enhance the generalization ability of the model, image data enhancement was performed on the data set. Random brightness enhancement, random contrast enhancement and sharpening, and Gamma enhancement were performed on the 2991 original Chinese offshore wind power images in the training set.

Based on the first aspect, further, the above-mentioned method of training and verifying the initial offshore wind power identification model using the offshore wind power sample set includes the following steps:

The initial offshore wind power identification model is trained and verified using the training set and the test set respectively.

Based on the first aspect, further, the above-mentioned method of constructing an initial offshore wind power identification model based on the YOLOv5 algorithm, CA attention mechanism and RFB multi-branch convolution module includes the following steps:

Introduce the CA attention mechanism into the Backbone module of the YOLOv5 algorithm and build the CS-CA module;

Introducing the RFB multi-branch convolution module into the backbone network of the YOLOv5 algorithm;

An initial offshore wind power identification model is constructed based on the YOLOv5 algorithm, CS-CA module and RFB multi-branch convolution module.

YOLOv5 (is an efficient single-stage target detection algorithm. The network structure consists of Input module, Backbone module, Neck module and Head module. Step 1: In the Input module, the YOLOv5s-CR model uses translation, sharpening, contrast enhancement, etc. , improve the model generalization ability and robustness. Step 2: The Backbone module includes Conv, CoT module and SPFF module, which is responsible for extracting image features. In the Backbone module, we add the CA attention mechanism to the C3 module to strengthen the visual backbone and improve positioning ability to achieve better performance. Step 3: The Neck network uses the path aggregation network (PANet) to enhance feature information and improve the detection performance of small targets. In addition, the RFB module is introduced to expand the receptive field and capture features through convolution kernels of different sizes. Information, control the ratio between receptive field size and eccentricity to generate a spatial array of receptive fields. Step 4: The Output module is mainly a prediction box with bounding box position, confidence, and category probability. YOLOv5s-CR improves model detection Efficiency and accuracy (the detection process and architecture of this model are shown in Figure 2).

One of the main challenges faced in the field of remote sensing target detection is to deal with target recognition and small target false alarms in complex backgrounds. The present invention introduces a coordinate attention (CA) module into the C3 module to significantly improve the ability to identify offshore wind power feature extraction and positioning. The design of this module cleverly integrates the position information of China's offshore wind power and the high sensitivity of the relationship between channels. By embedding the position information into the channel attention, a wider range of contextual information can be obtained. The structure of the CA module is shown in Figure 2(b). ) shown. For each channel of the input feature map This operation aggregates features along two spatial directions, producing direction-aware feature maps and , which are used to model long-term dependencies along the spatial direction and preserve precise position information along the other spatial direction, respectively. . After F1 operation (1×1 convolution kernel dimensionality reduction) and nonlinear activation, an intermediate feature map f is formed, and then the intermediate feature map f is decomposed into a height attention tensor and a width attention tensor. Through two 1×1 convolutions, the dimensionality operation is performed and combined with the sigmoid function to reduce the complexity and computational cost of the model, and obtain the attention vector sum of height and width. Finally, the input feature map X is combined with the attention weights of height and width respectively. and multiplied to generate the final attention feature map Y. The C3-CA module improves the detection performance of China's offshore wind power and reduces the false alarm rate by integrating channel and location information and using an attention mechanism. It provides an effective solution to the problem of remote sensing target detection in complex SAR backgrounds with almost no increase in computational overhead.

In order to improve the speed and accuracy in China's offshore wind power target detection tasks, the present invention introduces the RFB (ReceptiveFieldBlock) module. The multi-branch convolution layer is the core component of the RFB module. It cleverly uses a bottleneck structure to reduce the dimension of channel features through a 1x1 convolution layer with a stride of 2, and then introduces an nxn convolution layer. In order to effectively control the number of parameters and increase the depth of the nonlinear layer, the module uses two 3x3 convolutions to replace the 5x5 convolution, and introduces a 1x1 convolution layer without an activation function in the direct connection layer. In addition, the RFB module controls the eccentricity of each branch through dilated convolution technology, simulating the ratio between receptive field size and eccentricity, and finally concatenates and performs 1×1 convolution to generate a spatial array of receptive fields. Obtain higher resolution feature representation while maintaining a smaller number of parameters. Fusion of multiple branches with different convolution kernel sizes and expansion factors further improves feature extraction capabilities and provides strong support for module performance improvement. We integrate the RFB module into the lightweight YOLOv5s backbone network, which can effectively improve the quality of feature representation and accurately measure the relationship between receptive field size and eccentricity, making it more discriminative and robust. Compared with the traditional deep deepening backbone network, the RFB module introduces a more flexible feature extraction mechanism, which accelerates tasks while ensuring accuracy.

In other embodiments of the present invention, in order to deeply verify the performance of the proposed YOLOv5s-CR model, the SAR image data set of China's offshore wind power from 2015 to 2022 was tested year by year in a time series manner. The selected test data sets include SAR images of different sea areas, diverse underlying surface conditions, different wind turbine types, water depths and offshore distances, as well as complex land and ocean backgrounds that have not been separated by sea and land. We paid special attention to the typical offshore wind farms on the tidal flats of Jiangsu, and used the time series as the benchmark to display the identification and visualization results of offshore wind power in each year from 2015 to 2022. The details are shown in (a)-(h) in Figure 3. The red box represents offshore wind power, and (a)–(h) represent the test samples from 2015 to 2022 respectively. The results show that the model can identify China's offshore wind power targets in every year with almost no omissions, and has never misjudged adjacent offshore wind power targets as the same target, or misjudged a single Chinese offshore wind power target as two targets. . In addition, the model can still accurately identify China's offshore wind power in a complex background that has not been separated from land and sea. The experimental results comprehensively verify the generalization ability of our model on SAR images at different times, further proving its high reliability. The YOLOv5s-CR model continues to perform well in time series experiments, fully proving its excellent performance in temporal generalization and highlighting the potential for robustness and sustainable optimization.

In order to further study the geographical generalization of the YOLOv5s-CR model, we expanded the application scope of the model and used it for identification and detection of typical offshore wind farms in other countries around the world, including the United Kingdom, Germany, Denmark, France, Belgium and other countries. . By conducting model identification on SAR image data of offshore wind farms in different geographical environments, the stability and broad adaptability of the model on a global scale are verified.

We selected representative SAR image samples from offshore wind farm data in each country to create test sets of different scales. In order to examine the applicability of the model more comprehensively, these images were not segmented into land and sea. We apply the YOLOv5s-CR model to these selected image data to detect offshore wind power targets in an automated manner. Some of the detection results are shown in Figure 4(a)-(n). After analyzing the identification results, we observed that the YOLOv5s-CR model can accurately identify most offshore wind power. However, there are also some missed detections (Figure 4(d)). In complex images where land and ocean exist at the same time, false detections and missed detections are more likely to occur. Buildings on land (Figure 4(l)), oceans The embankment in (Fig. 4(m)) interferes with the model detection. This phenomenon may be related to the noise interference present in the image and the complex interaction between the wind farm and the surrounding environment. Based on comprehensive model testing and analysis of offshore wind farm data sets from other countries around the world, we believe that the YOLOv5s-CR model can show high recognition accuracy and performance stability in different geographical environments, and has high geographical generalization. and broad applicability.

As shown in Figure 6, in the second aspect, the embodiment of the present invention provides an offshore wind power identification system based on the yolo algorithm, including a sample set production module 100, an initial model construction module 200, a model training and verification module 300, and an offshore wind power identification module. 400, of which:

The sample set production module 100 is used to collect remote sensing images of offshore wind farms in multiple regions and create an offshore wind power sample set;

The initial model building module 200 is used to build an initial offshore wind power identification model based on the YOLOv5 algorithm, CA attention mechanism and RFB multi-branch convolution module;

The model training and verification module 300 is used to train and verify the initial offshore wind power identification model using the offshore wind power sample set to obtain the target offshore wind power identification model;

The offshore wind power identification module 400 is used to obtain real-time offshore wind power images and identify offshore wind power targets using a target offshore wind power identification model.

This system uses multiple modules such as the sample set production module 100, the initial model construction module 200, the model training and verification module 300, and the offshore wind power identification module 400 to construct an offshore wind power identification model to accurately and stably detect offshore wind power, effectively improving the Detection efficiency and detection accuracy. This invention creates a deep learning training data set suitable for China's offshore wind power target recognition, optimizes and improves it based on the YOLOv5 algorithm in the YOLO algorithm, introduces the CA attention mechanism and RFB multi-branch convolution module, and constructs effective offshore wind power recognition Model (YOLOv5s-CR model), the coordinate attention CA module is added to improve the positioning ability of the model, and the context enhancement module RFB is integrated to increase the receptive field and reduce the error detection rate of the model in the large-scale complex sea surface background, which can realize the rapid development of China's offshore wind power detection and precise positioning. Based on the constructed offshore wind power identification model, offshore wind power is accurately and stably detected, effectively improving detection efficiency and detection accuracy. The offshore wind power identification model constructed by the present invention has good temporal generalization and geographical generalization performance.

As shown in FIG. 7 , in a third aspect, an embodiment of the present application provides an electronic device, which includes a memory 101 for storing one or more programs; and a processor 102 . When one or more programs are executed by the processor 102, the method as in any one of the above first aspects is implemented.

It also includes a communication interface 103. The memory 101, the processor 102 and the communication interface 103 are directly or indirectly electrically connected to each other to realize data transmission or interaction. For example, these components may be electrically connected to each other through one or more communication buses or signal lines. The memory 101 can be used to store software programs and modules. The processor 102 executes the software programs and modules stored in the memory 101 to perform various functional applications and data processing. The communication interface 103 can be used to communicate signaling or data with other node devices.

The memory 101 may be, but is not limited to, random access memory (Random Access Memory, RAM), read only memory (Read Only Memory, ROM), programmable read-only memory (Programmable Read-Only Memory, PROM), erasable memory. Read-only memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable read-only memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.

The processor 102 may be an integrated circuit chip with signal processing capabilities. The processor 102 can be a general-purpose processor, including a central processing unit (CPU), a network processor (Network Processor, NP), etc.; it can also be a digital signal processor (Digital Signal Processing, DSP), an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components.

In the embodiments provided in this application, it should be understood that the disclosed methods and systems can also be implemented in other ways. The method and system embodiments described above are only illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show possible implementation systems of the methods and systems, methods and computer program products according to multiple embodiments of the present application. Architecture, functionality and operations. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code that contains one or more components for implementing the specified logical function(s). Executable instructions. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two consecutive blocks may actually execute substantially in parallel, or they may sometimes execute in the reverse order, depending on the functionality involved. It will also be noted that each block of the block diagram and/or flowchart illustration, and combinations of blocks in the block diagram and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts. , or can be implemented using a combination of specialized hardware and computer instructions.

In addition, each functional module in each embodiment of the present application can be integrated together to form an independent part, each module can exist alone, or two or more modules can be integrated to form an independent part.

In a fourth aspect, embodiments of the present application provide a computer-readable storage medium on which a computer program is stored. When the computer program is executed by the processor 102, the method in any one of the above-mentioned first aspects is implemented. If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the existing technology or the part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other various media that can store program code.

The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

It is obvious to those skilled in the art that the present application is not limited to the details of the above-described exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be regarded as illustrative and non-restrictive from any point of view, and the scope of the application is defined by the appended claims rather than the above description, and it is therefore intended that all claims falling within the claims All changes within the meaning and scope of the equivalent elements are included in this application. Any reference signs in the claims shall not be construed as limiting the claim in question.

Claims (9)

1. An offshore wind power identification method based on the yolo algorithm, which is characterized by including the following steps: Collect remote sensing images of offshore wind farms in multiple areas and create an offshore wind power sample collection; Build an initial offshore wind power identification model based on the YOLOv5 algorithm, CA attention mechanism and RFB multi-branch convolution module; Use the offshore wind power sample set to train and verify the initial offshore wind power identification model to obtain the target offshore wind power identification model; Obtain real-time offshore wind images and use the target offshore wind power identification model to identify offshore wind power targets. 2. An offshore wind power identification method based on the yolo algorithm according to claim 1, characterized in that the method of making an offshore wind power sample set includes the following steps: Preprocess remote sensing images of offshore wind farms; Perform mean calculation on the preprocessed offshore wind farm remote sensing images and optimize the offshore wind farm remote sensing images; Perform sample labeling on the optimized offshore wind farm remote sensing images to obtain an offshore wind power sample test set and an offshore wind power sample verification set; Use the ArcGIS Pro image processing tool to create sample slices of the offshore wind power sample test set and offshore wind power sample verification set, and write the location information and category information of all samples into the corresponding xml files; Select some sample slice data as the training set and test set to form a data set in VOC format. 3. An offshore wind power identification method based on the yolo algorithm according to claim 2, characterized in that the method for preprocessing remote sensing images of offshore wind farms includes the following steps: Update the orbit state vector of offshore wind farm remote sensing images, and use the corrected orbit file to update orbit metadata for the ground-distance multi-view image GRD in offshore wind farm remote sensing images; Suppress low-intensity noise and invalid data at the edge of the GRD scene by masking low-value pixels to eliminate additional noise in sub-strips of offshore wind farm remote sensing images; The sensor calibration parameters in the metadata are used to calculate the backscatter intensity for radiometric calibration, and the digital elevation model is used to perform orthorectification of remote sensing images of offshore wind farms. 4. An offshore wind power identification method based on the yolo algorithm according to claim 2, characterized in that it further includes the following steps: Perform image enhancement on the images in the dataset. 5. An offshore wind power identification method based on the yolo algorithm according to claim 2, characterized in that the method of using an offshore wind power sample set to train and verify an initial offshore wind power identification model includes the following steps: The initial offshore wind power identification model is trained and verified using the training set and the test set respectively. 6. An offshore wind power identification method based on the yolo algorithm according to claim 1, characterized in that the method of constructing an initial offshore wind power identification model based on the YOLOv5 algorithm, CA attention mechanism and RFB multi-branch convolution module includes Following steps: Introduce the CA attention mechanism into the Backbone module of the YOLOv5 algorithm and build the CS-CA module; Introducing the RFB multi-branch convolution module into the backbone network of the YOLOv5 algorithm; An initial offshore wind power identification model is constructed based on the YOLOv5 algorithm, CS-CA module and RFB multi-branch convolution module. 7. An offshore wind power identification system based on the yolo algorithm, which is characterized by including a sample set production module, an initial model construction module, a model training and verification module, and an offshore wind power identification module, wherein: The sample set production module is used to collect remote sensing images of offshore wind farms in multiple regions and create offshore wind power sample sets; Initial model building module, used to build an initial offshore wind power identification model based on the YOLOv5 algorithm, CA attention mechanism and RFB multi-branch convolution module; The model training and verification module is used to train and verify the initial offshore wind power identification model using the offshore wind power sample set to obtain the target offshore wind power identification model; The offshore wind power identification module is used to obtain real-time offshore wind power images and use the target offshore wind power identification model to identify offshore wind power targets. 8. An electronic device, characterized in that it includes: Memory, used to store one or more programs; processor; When the one or more programs are executed by the processor, the method as claimed in any one of claims 1-6 is implemented. 9. A computer-readable storage medium with a computer program stored thereon, characterized in that when the computer program is executed by a processor, the method according to any one of claims 1-6 is implemented.