Basketball detection method, system and equipment based on artificial intelligence

Abstract

The invention discloses a basketball detection method, system and equipment based on artificial intelligence, and belongs to the technical field of basketball recognition. The method comprises the following steps: acquiring site environment data, equipment performance data and basketball image data; performing comprehensive analysis according to the field environment data to obtain a field environment evaluation value of each monitoring time point, and adjusting the basketball image data according to the field environment evaluation value of each monitoring time point to obtain a corrected basketball image of each monitoring time point; the equipment performance index of each monitoring time point is obtained through comprehensive analysis according to the equipment performance data, and the image accuracy evaluation value of each monitoring time point is obtained through comprehensive analysis according to the corrected basketball image of each monitoring time point, the equipment performance index of each monitoring time point and the site environment evaluation value of each monitoring time point. And adjustment feedback is carried out according to the image accuracy evaluation value, so that the image calibration precision and the detection accuracy are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of basketball recognition, and in particular to a basketball detection method, system and device based on artificial intelligence. Background Art

[0002] The principle of artificial intelligence is to simulate the technology and theory of human intelligence, so that machines can think, make decisions and act autonomously, and achieve intelligent behavior like humans. Artificial intelligence has gone through many waves of development, from the initial symbolism to the later connectionism, and then to the current deep learning. Each step marks the continuous progress of artificial intelligence technology.

[0003] Existing basketball motion trajectory detection methods are mainly achieved through various technical means such as GPS technology, camera capture technology, sensor technology, data analysis and visualization technology, etc. At the same time, there are certain defects in signal reception, occlusion, imaging quality, data processing complexity, privacy protection, sensor defects, data fusion, wearing comfort and algorithm limitations.

[0004] For example, the patent application with publication number: CN112446333A discloses a ball target tracking method and system based on re-detection, including: initializing the constructed tracker, verifier and detector to obtain a position blacklist; obtaining the detection image input to the tracker, using the target tracking algorithm to identify the output predicted target ball position; transmitting the output of the tracker to the verifier, using the visual tracking algorithm to score the output of the tracker, and executing the detection of the next frame of the image if the score is qualified, otherwise executing the next step; using the target detection algorithm to perform global detection, and obtaining the position of the target ball after filtering the blacklist position from the global detection result.

[0005] For example, the invention patent announcement with announcement number: CN106991359B discloses an algorithm for tracking basketballs in ball game videos in panoramic mode, including: 1) generating scene annotations; 2) detecting candidate balls; 3) generating short trajectories; 4) correcting and filtering short trajectories; 5) connecting short trajectories into long trajectories. The present invention provides an algorithm for tracking basketballs in ball game videos in panoramic mode, which utilizes some prior information of the court in basketball sports (such as the position of the court, the position of the basket, the average height of the players, etc.), and several main sports forms of basketball (holding the ball, bouncing the ball, passing the ball, shooting), extracts the corresponding motion features, and adds these motion features to the tracking of the basketball.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the prior art, when detecting the motion trajectory of a basketball outdoors, there is a problem of low accuracy in detecting the basketball in the basketball image due to poor image calibration accuracy. Summary of the invention

[0008] The present invention provides an artificial intelligence-based basketball detection method, system and device, which solves the problem of low basketball detection accuracy due to poor image calibration accuracy when detecting basketball motion trajectories outdoors in the prior art, thereby improving image calibration accuracy and detection accuracy.

[0009] The present invention provides an artificial intelligence-based basketball detection method, comprising the following steps: acquiring venue environment data, equipment performance data and basketball image data; obtaining a venue environment evaluation value at each monitoring time point according to a comprehensive analysis of the venue environment data, and adjusting the basketball image data according to the venue environment evaluation value at each monitoring time point to obtain a corrected basketball image at each monitoring time point; obtaining an equipment performance index at each monitoring time point according to a comprehensive analysis of the equipment performance data, obtaining an image accuracy evaluation value at each monitoring time point according to a comprehensive analysis of the corrected basketball image at each monitoring time point, the equipment performance index at each monitoring time point and the venue environment evaluation value at each monitoring time point, and performing adjustment feedback according to the image accuracy evaluation value.

[0010] Furthermore, the step of obtaining the site environment assessment value at each monitoring time point based on a comprehensive analysis of the site environment data includes: the site environment data includes light intensity, illuminance data and light coverage, wherein the illuminance data includes vertical illuminance and horizontal illuminance; the vertical illuminance and horizontal illuminance of each position point are monitored, and the reference light intensity, allowable deviation light intensity, allowable deviation vertical illuminance, allowable deviation horizontal illuminance and critical light coverage are obtained from the basketball detection database; and the site environment assessment value at each monitoring time point is obtained through comprehensive analysis.

[0011] Furthermore, the step of adjusting the basketball image data according to the site environment assessment value at each monitoring time point to obtain a corrected basketball image at each monitoring time point includes: the basketball image data includes a basketball image at each monitoring time point; obtaining a site environment assessment threshold from a basketball detection database, comparing the site environment assessment value at each monitoring time point with the site environment assessment threshold, if the site environment assessment value at a certain monitoring time point is less than the site environment assessment threshold, subtracting the site environment assessment threshold from the site environment assessment value at the monitoring time point to obtain a deviation site environment assessment value, and adjusting the basketball image at the monitoring time point according to the deviation site environment assessment value to obtain a corrected basketball image at the monitoring time point; if the site environment assessment value at a certain monitoring time point is greater than or equal to the site environment assessment threshold, no additional processing is performed; and statistics are obtained to obtain the corrected basketball image at each monitoring time point.

[0012] Furthermore, the step of adjusting the basketball image at the monitoring time point according to the deviation venue environment assessment value includes: matching the deviation venue environment assessment value with the contrast adjustment amplitude level corresponding to each deviation venue environment assessment value preset in the basketball detection database to obtain the basketball image contrast adjustment amplitude level, and adjusting the contrast of the basketball image according to the basketball image contrast adjustment amplitude level.

[0013] Furthermore, the step of obtaining the equipment performance index at each monitoring time point based on comprehensive analysis of the equipment performance data includes: the equipment performance data includes battery power, resolution and frame rate; obtaining the critical battery power, critical resolution, reference frame rate and allowable deviation frame rate from the basketball detection database; and obtaining the equipment performance index at each monitoring time point through comprehensive analysis.

[0014] Furthermore, the step of obtaining the image accuracy evaluation value at each monitoring time point by comprehensive analysis based on the corrected basketball image at each monitoring time point, the equipment performance index at each monitoring time point and the site environment evaluation value at each monitoring time point includes: extracting the basketball position, basketball movement angle and basketball movement speed at each monitoring time point from the corrected basketball image at each monitoring time point, and jointly marking them as basketball movement data at each monitoring time point; comparing the basketball movement data at each monitoring time point with the basketball movement data at the adjacent previous monitoring time point, respectively, to obtain the position change parameters, movement angle change parameters and movement speed change parameters at each monitoring time point; obtaining the reference position change parameters and reference movement angle change parameters at each monitoring time point according to the basketball movement speed matching at each monitoring time point, and obtaining the allowable deviation position change parameters, allowable deviation movement angle change parameters, allowable deviation movement speed change parameters, reference movement speed change parameters, critical equipment performance index and critical site environment evaluation value from the basketball detection database; and obtaining the image accuracy evaluation value at each monitoring time point by comprehensive analysis.

[0015] Furthermore, the image accuracy evaluation value at each monitoring time point is obtained as follows:

[0016] ;

[0017] In the formula, represents the image accuracy evaluation value at the i-th monitoring time point, Indicates the impact factor of image accuracy evaluation corresponding to the basketball position, Indicates the impact factor of image accuracy evaluation corresponding to basketball motion angle, Indicates the impact factor of image accuracy evaluation corresponding to basketball movement speed, Indicates the image accuracy assessment impact factor corresponding to the device performance index, Indicates the image accuracy assessment impact factor corresponding to the site environment assessment value, represents the position change parameter at the i-th monitoring time point, represents the reference position variation parameter, Indicates the allowable deviation position change parameter, represents the motion angle variation parameter at the i-th monitoring time point, represents the reference motion angle variation parameter, Indicates the allowable deviation motion angle variation parameter, represents the motion rate variation parameter at the i-th monitoring time point, represents the reference motion rate variation parameter, Indicates the allowable deviation motion rate change parameter, represents the equipment performance index at the ith monitoring time point, represents the critical equipment performance index, represents the site environmental assessment value at the ith monitoring time point, Represents the critical site environmental assessment value, where i is the number of each monitoring time point, i=1, 2, 3, ..., N, and N is the total number of monitoring time points.

[0018] Furthermore, the step of adjusting feedback according to the image accuracy evaluation value includes: obtaining an image accuracy evaluation threshold from a basketball detection database; comparing the image accuracy evaluation value with the image accuracy evaluation threshold, if the image accuracy evaluation value is less than the image accuracy evaluation threshold, further performing image correction processing on the corrected basketball image, if the image accuracy evaluation value is greater than or equal to the image accuracy evaluation threshold, no additional processing is performed.

[0019] An embodiment of the present application provides an artificial intelligence-based basketball detection system, including a data acquisition module, an image correction module, an image adjustment module and a basketball detection database; wherein the data acquisition module is used to acquire venue environment data, equipment performance data and basketball image data; the image correction module is used to obtain a venue environment evaluation value at each monitoring time point based on a comprehensive analysis of the venue environment data, and adjust the basketball image data according to the venue environment evaluation value at each monitoring time point to obtain a corrected basketball image at each monitoring time point; the image adjustment module is used to obtain an equipment performance index at each monitoring time point based on a comprehensive analysis of the equipment performance data, obtain an image accuracy evaluation value at each monitoring time point based on a comprehensive analysis of the corrected basketball image at each monitoring time point, the equipment performance index at each monitoring time point and the venue environment evaluation value at each monitoring time point, and perform adjustment feedback based on the image accuracy evaluation value.

[0020] An embodiment of the present application provides an artificial intelligence-based basketball detection electronic device, comprising: a processor, and a memory for storing instructions executable by the processor; when the processor is configured to execute the instructions, the electronic device implements an artificial intelligence-based basketball detection method.

[0021] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0022] 1. The present invention provides an artificial intelligence-based basketball detection method, system and device, which can perform comprehensive analysis based on venue environment data, equipment performance data and basketball image data, and adjust feedback based on the evaluation results, thereby effectively improving the accuracy and reliability of basketball detection.

[0023] 2. The present invention accurately adjusts the basketball image data according to the venue environment assessment value at each monitoring time point, thereby effectively compensating for the negative impact of venue lighting changes on the basketball image quality, thereby realizing the correction and optimization of the basketball image at each monitoring time point, providing a more reliable basis for subsequent image accuracy evaluation and adjustment feedback.

[0024] 3. The present invention adjusts feedback according to the image accuracy evaluation value, so as to timely identify and correct errors caused by factors such as changes in the venue environment and fluctuations in equipment performance during basketball image detection, thereby achieving continuous optimization and improvement of the basketball motion trajectory detection system, and providing more accurate data support for basketball games or training. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A flow chart of a basketball detection method based on artificial intelligence provided in an embodiment of the present application.

[0026] Figure 2 A graph showing changes in equipment performance index for an artificial intelligence-based basketball detection method provided in an embodiment of the present application.

[0027] Figure 3 A schematic diagram of the structure of a basketball detection system based on artificial intelligence provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The embodiments of the present application provide an artificial intelligence-based basketball detection method, system and device, which solve the problem of low basketball detection accuracy due to poor image calibration accuracy when detecting basketball motion trajectories outdoors in the prior art. The site environment evaluation value at each monitoring time point is obtained through comprehensive analysis of the site environment data, and the basketball image data is adjusted according to the site environment evaluation value at each monitoring time point to obtain a corrected basketball image at each monitoring time point; the equipment performance index at each monitoring time point is obtained through comprehensive analysis of the equipment performance data, and the image accuracy evaluation value at each monitoring time point is obtained through comprehensive analysis based on the corrected basketball image at each monitoring time point, the equipment performance index at each monitoring time point and the site environment evaluation value at each monitoring time point; adjustment feedback is performed based on the image accuracy evaluation value, thereby improving image calibration accuracy and detection accuracy.

[0029] The technical solution in the embodiment of the present application is to solve the problem of low basketball detection accuracy due to poor image calibration accuracy when detecting basketball motion trajectory outdoors. The overall idea is as follows:

[0030] By acquiring venue environment data, equipment performance data and basketball image data; obtaining the venue environment assessment value at each monitoring time point based on comprehensive analysis of the venue environment data, and adjusting the basketball image data according to the venue environment assessment value at each monitoring time point to obtain the corrected basketball image at each monitoring time point; obtaining the equipment performance index at each monitoring time point based on comprehensive analysis of the equipment performance data, obtaining the image accuracy assessment value at each monitoring time point based on comprehensive analysis of the corrected basketball image at each monitoring time point, the equipment performance index at each monitoring time point and the venue environment assessment value at each monitoring time point, and performing adjustment feedback based on the image accuracy assessment value, comprehensive monitoring and accurate evaluation of basketball sports scenes are achieved.

[0031] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0032] like Figure 1As shown, it is a flow chart of a basketball detection method based on artificial intelligence provided by an embodiment of the present application, and the method includes the following steps: acquiring venue environment data, equipment performance data and basketball image data; obtaining venue environment evaluation values ​​at each monitoring time point according to comprehensive analysis of the venue environment data, and adjusting the basketball image data according to the venue environment evaluation values ​​at each monitoring time point to obtain corrected basketball images at each monitoring time point; obtaining equipment performance indexes at each monitoring time point according to comprehensive analysis of the equipment performance data, obtaining image accuracy evaluation values ​​at each monitoring time point according to comprehensive analysis of the corrected basketball images at each monitoring time point, the equipment performance indexes at each monitoring time point and the venue environment evaluation values ​​at each monitoring time point, and performing adjustment feedback according to the image accuracy evaluation values.

[0033] In this embodiment, the present invention can comprehensively acquire a variety of data related to basketball, including venue environment, equipment performance, and basketball images, thereby realizing all-round monitoring of basketball sports scenes; at the same time, it can provide accurate and reliable basketball sports data for coaches, referees, and athletes, helping them to better understand the situation of the game or training, formulate more scientific and reasonable tactics and training plans, and provide strong data support for subsequent decision-making.

[0034] In addition, the basketball detection database is used to store relevant data of the artificial intelligence-based basketball detection method, including: reference light intensity, allowable deviation light intensity, allowable deviation vertical illuminance, allowable deviation horizontal illuminance, critical light coverage, site environment assessment influencing factors corresponding to light intensity, site environment assessment thresholds, and contrast adjustment amplitude levels corresponding to each deviation site environment assessment value, etc. The data in the basketball detection database can be obtained through professional basketball databases or comprehensive sports databases such as BasketballReference and Synergy Sports, or through cooperation with basketball clubs, associations or media organizations.

[0035] Furthermore, the step of obtaining the site environment assessment value at each monitoring time point based on a comprehensive analysis of the site environment data includes: the site environment data includes light intensity, illuminance data and light coverage, wherein the illuminance data includes vertical illuminance and horizontal illuminance; the vertical illuminance and horizontal illuminance of each position point are monitored, and the reference light intensity, allowable deviation light intensity, allowable deviation vertical illuminance, allowable deviation horizontal illuminance and critical light coverage are obtained from the basketball detection database; and the site environment assessment value at each monitoring time point is obtained through comprehensive analysis.

[0036] The method for obtaining the site environmental assessment value at each monitoring time point is as follows: ;

[0037] In the formula, represents the site environmental assessment value at the ith monitoring time point, Indicates the site environment assessment impact factor corresponding to the light intensity, Indicates the site environment assessment impact factor corresponding to the vertical illumination, Indicates the site environment assessment impact factor corresponding to the horizontal illumination, Indicates the site environment assessment impact factor corresponding to the light coverage rate, represents the light intensity at the ith monitoring time point, represents the reference light intensity, Indicates the allowable deviation of light intensity, represents the vertical illuminance of the jth position at the ith monitoring time point, Indicates the allowable deviation of vertical illuminance, represents the horizontal illumination at the jth position at the ith monitoring time point, Indicates the allowable deviation of horizontal illumination, represents the light coverage at the ith monitoring time point, represents the critical light coverage, where i is the number of each monitoring time point, i=1, 2, 3, ..., N, N is the total number of monitoring time points, and j is the number of each location point, j=1, 2, 3, ..., M, M is the total number of location points.

[0038] , , and The venue environment assessment influencing factors corresponding to the light intensity, vertical illuminance, horizontal illuminance and light coverage ratio preset in the basketball detection database are numerical indicators that measure the influence of the above-mentioned light factors on the venue environment assessment value. Specifically, there is a mapping relationship table for each of the light intensity, vertical illuminance, horizontal illuminance and light coverage ratio, which records each possible light factor value and its corresponding venue environment assessment influencing factor. These mapping relationships can be one-to-one or many-to-one. For example, when it is necessary to evaluate the venue environment assessment value at a certain monitoring time point, the measured light intensity, vertical illuminance, horizontal illuminance and light coverage ratio can be input into their respective corresponding mapping relationship tables, and the venue environment assessment influencing factors corresponding to these values ​​can be quickly found, where the value range of the influencing factor is between 0 and 1.

[0039] In this embodiment, the light intensity can be measured by using a light meter; the illuminance data can be obtained by measuring at various locations using professional equipment such as a light meter; the light coverage rate can be calculated by measuring the ratio of the area illuminated by the light source to the total area. The change in illuminance data will affect the distribution of light intensity. For example, the light intensity at the center of a light source with a narrow beam angle is prominent, and the light spot range is small, which is suitable for key lighting; the higher the light intensity, the larger the area that the light source can illuminate (when the power and type of the light source remain unchanged), thereby improving the light coverage rate; the optimization of illuminance data can ensure that the light better covers the target area, reduce light waste, and improve the light coverage rate. The lighting evaluation value of the site environment obtained through comprehensive analysis helps to determine whether the lighting in the current image meets the site and image detection requirements, and whether there is a problem of insufficient lighting or excessive lighting.

[0040] Furthermore, the step of adjusting the basketball image data according to the site environment assessment value at each monitoring time point to obtain a corrected basketball image at each monitoring time point includes: the basketball image data includes a basketball image at each monitoring time point; obtaining a site environment assessment threshold from a basketball detection database, comparing the site environment assessment value at each monitoring time point with the site environment assessment threshold, if the site environment assessment value at a certain monitoring time point is less than the site environment assessment threshold, subtracting the site environment assessment threshold from the site environment assessment value at the monitoring time point to obtain a deviation site environment assessment value, and adjusting the basketball image at the monitoring time point according to the deviation site environment assessment value to obtain a corrected basketball image at the monitoring time point; if the site environment assessment value at a certain monitoring time point is greater than or equal to the site environment assessment threshold, no additional processing is performed; and statistics are obtained to obtain the corrected basketball image at each monitoring time point.

[0041] In this embodiment, the venue environment assessment threshold is used to determine whether the venue environment assessment value reaches or exceeds the standard value that has a significant impact on the quality of the basketball image. By comparing the venue environment assessment value with the threshold and adjusting the basketball image according to the deviation value, the impact of venue environment factors on the image quality, such as light changes, shadow interference, etc., can be eliminated or reduced, thereby improving the accuracy and clarity of the image. When the venue environment assessment value is less than the threshold, it is considered that the basketball image needs to be adjusted; when the venue environment assessment value is greater than or equal to the threshold, it is considered that the impact of the venue environment on the quality of the basketball image is within an acceptable range and no additional processing is required.

[0042] Furthermore, the step of adjusting the basketball image at the monitoring time point according to the deviation venue environment assessment value includes: matching the deviation venue environment assessment value with the contrast adjustment amplitude level corresponding to each deviation venue environment assessment value preset in the basketball detection database, wherein the deviation venue environment assessment value and the contrast adjustment amplitude level corresponding to each deviation venue environment assessment value preset in the basketball detection database form a mapping set, inputting the mapping set according to the deviation venue environment assessment value to obtain the basketball image contrast adjustment amplitude level corresponding to the deviation venue environment assessment value, and adjusting the contrast of the basketball image according to the basketball image contrast adjustment amplitude level.

[0043] In this embodiment, the deviation site environment assessment value refers to the difference between the site environment assessment value and the site environment assessment threshold, which is used to quantify the specific degree of influence of the site environment on the quality of the basketball image. When adjusting the contrast of the basketball image according to the basketball image contrast adjustment amplitude level, the basketball image contrast adjustment amplitude level includes a first adjustment level, a second adjustment level, and a third adjustment level. When it is at the first adjustment level, it means that the environment indicated by the deviation site environment assessment value has little influence on the contrast of the basketball image, but a certain degree of adjustment is still required to ensure the image quality. Contrast enhancement methods such as power law transformation can be used, but the transformation intensity should be kept small when applied, and the contrast of the image should be slightly increased or decreased to fine-tune the difference between light and dark areas in the image. For example, for power transformation, the exponent range fluctuation interval is [0.95,1.05]; when it is in the third adjustment level, the second adjustment level is [0.95,1.05]. When it is at the second adjustment level, it indicates that the environment has a significant impact on the contrast of the basketball image and requires a larger adjustment. For power transformation, the exponent range fluctuates between [0.8, 1.2]. If there are too bright or too dark areas in the image, the color scale adjustment method can be used to stretch the brightness of these areas to a more appropriate range. When it is at the third adjustment level, it indicates that the environment has a very large impact on the contrast of the basketball image and requires a large adjustment to improve the image quality. For power transformation, the exponent range fluctuates between [0.6, 1.4]. If there is severe noise or distortion in the image, noise removal or image smoothing can be performed before or after contrast adjustment. For example, the deviation field environment assessment value of the first adjustment level ranges from (0, 0.1], the deviation field environment assessment value of the second adjustment level ranges from (0.1, 0.2], the deviation field environment assessment value of the third adjustment level ranges from greater than 0.2, and the deviation field environment assessment value at a certain monitoring time point is 0.15, so the basketball image contrast adjustment amplitude level corresponding to the deviation field environment assessment value is the second adjustment level. Currently, by adjusting the contrast of the basketball image according to the deviation field environment assessment value, the image quality can be significantly improved, making the image clearer and more vivid. This helps coaches, athletes or spectators to better observe and analyze the game situation; the corrected basketball image has higher data availability and can be used for more accurate game analysis, tactical formulation and athlete training evaluation.

[0044] Furthermore, the step of obtaining the equipment performance index at each monitoring time point based on comprehensive analysis of the equipment performance data includes: the equipment performance data includes battery power, resolution and frame rate; obtaining the critical battery power, critical resolution, reference frame rate and allowable deviation frame rate from the basketball detection database; and obtaining the equipment performance index at each monitoring time point through comprehensive analysis.

[0045] The device performance index at each monitoring time point is obtained as follows:

[0046] ;

[0047] In the formula, represents the equipment performance index at the ith monitoring time point, Indicates the device performance index impact factor corresponding to the battery power. Indicates the device performance index impact factor corresponding to the resolution, Indicates the device performance index impact factor corresponding to the frame rate, represents the battery power at the ith monitoring time point, Indicates critical battery level, represents the resolution of the ith monitoring time point, represents the critical resolution, represents the frame rate at the i-th monitoring time point, Indicates the reference frame rate, Indicates the allowed deviation from the frame rate.

[0048] , and They are the device performance index influencing factors corresponding to the battery power, resolution and frame rate preset in the basketball detection database, and these influencing factors are numerical indicators that measure the influence of the above parameters on the device performance index. Specifically, there is a mapping relationship table for each battery power, resolution and frame rate, which records each possible parameter value and its corresponding device performance index influencing factor. These mapping relationships can be one-to-one or many-to-one. In actual applications, when it is necessary to evaluate the device performance index at a certain monitoring time point, the measured battery power, resolution and frame rate can be input into their respective corresponding mapping relationship tables, and the device performance index influencing factors corresponding to these values ​​can be quickly found, where the value range of the influencing factor is between 0 and 1.

[0049] In this embodiment, the battery power information can be obtained through the device's own battery status indicator or third-party battery management software; the resolution can be directly queried using tools such as ImageMagick and TinyPNG; the frame rate can be measured by professional performance testing software such as Fraps. There is an indirect correlation between the battery power and the resolution and frame rate. High resolution and high frame rate usually require more power to drive, so they will cause greater consumption of the battery power. The device performance index obtained through comprehensive analysis can comprehensively evaluate the performance of the device and put forward suggestions for performance optimization, such as reducing the resolution or frame rate to extend battery life, or upgrading the hardware configuration to improve performance.

[0050] Set the device performance index impact factor corresponding to the battery power to 0.3, the device performance index impact factor corresponding to the resolution to 0.3, the device performance index impact factor corresponding to the frame rate to 0.4, the battery power to 80%, the critical battery power to 20%, the resolution to 1080P, the critical resolution to 1080P, and the reference frame rate to 30 , the allowed deviation frame rate is set to 5 , when the frame rate is continuously increasing, the device performance index is calculated. Table 1 shows the device performance index data table of the basketball detection method based on artificial intelligence.

[0051] Table 1 Equipment performance index data table of basketball detection method based on artificial intelligence

[0052] like Figure 2 As shown in Table 1 and Figure 2 It can be seen that when the device performance index influencing factor corresponding to the battery power, the device performance index influencing factor corresponding to the resolution, the device performance index influencing factor corresponding to the frame rate, the battery power, the critical battery power, the resolution, the critical resolution, the reference frame rate and the allowable deviation frame rate remain unchanged, and the frame rate continues to increase, the closer the frame rate is to the reference frame rate, the greater the device performance index, and vice versa.

[0053] Furthermore, the step of obtaining the image accuracy evaluation value at each monitoring time point by comprehensive analysis based on the corrected basketball image at each monitoring time point, the equipment performance index at each monitoring time point and the site environment evaluation value at each monitoring time point includes: extracting the basketball position, basketball movement angle and basketball movement speed at each monitoring time point from the corrected basketball image at each monitoring time point, and jointly marking them as basketball movement data at each monitoring time point; comparing the basketball movement data at each monitoring time point with the basketball movement data at the adjacent previous monitoring time point, respectively, to obtain the position change parameters, movement angle change parameters and movement speed change parameters at each monitoring time point; obtaining the reference position change parameters and reference movement angle change parameters at each monitoring time point according to the basketball movement speed matching at each monitoring time point, and obtaining the allowable deviation position change parameters, allowable deviation movement angle change parameters, allowable deviation movement speed change parameters, reference movement speed change parameters, critical equipment performance index and critical site environment evaluation value from the basketball detection database; and obtaining the image accuracy evaluation value at each monitoring time point by comprehensive analysis.

[0054] In this embodiment, the position of the basketball is a three-dimensional vector. The center point of the basketball court can be set as the origin, the direction perpendicular to the ground can be set as the z-axis, the basketball court dividing line can be set as the x-axis, the line perpendicular to the x-axis on the ground can be set as the y-axis, and the center point of the basketball can be regarded as the basketball position point; the basketball movement angle can be obtained by installing a sensor on the basketball to extract the basketball tilt angle information; the basketball movement rate can be directly extracted from the sensor data. The position change parameter of each monitoring time point can be obtained by extracting the straight-line distance between the basketball position point at each monitoring time point and the basketball position point adjacent to the previous monitoring time point; and the movement angle change parameter and movement rate change parameter of each monitoring time point are directly obtained by taking the absolute value of the difference between the basketball movement angle and basketball movement rate at a certain monitoring time point and the basketball movement angle and basketball movement rate at the previous monitoring time point. The corrected basketball image is obtained by adjusting the contrast in the original basketball image to eliminate or reduce the impact of the venue environment and equipment performance on the image quality; the equipment performance index reflects the performance status of the shooting equipment, and the quality of the equipment performance directly affects the clarity and stability of the captured image; the venue environment assessment value is a comprehensive assessment of the venue light, and these environmental factors will also affect the quality of the captured image. The image accuracy assessment value obtained by comprehensive analysis is a quantitative representation of the accuracy of the basketball image at each monitoring time point, reflecting the reliability and accuracy of the basketball motion data in the image. Based on accurate image data, the movement characteristics of basketball, such as movement trajectory, speed changes, etc., can be further analyzed to provide strong support for game analysis, tactical formulation, etc.

[0055] Furthermore, the image accuracy evaluation value at each monitoring time point is obtained as follows:

[0056] ;

[0057] In the formula, represents the image accuracy evaluation value at the i-th monitoring time point, Indicates the impact factor of image accuracy evaluation corresponding to the basketball position, Indicates the impact factor of image accuracy evaluation corresponding to basketball motion angle, Indicates the impact factor of image accuracy evaluation corresponding to basketball movement speed, Indicates the image accuracy assessment impact factor corresponding to the device performance index, Indicates the image accuracy assessment impact factor corresponding to the site environment assessment value, represents the position change parameter at the i-th monitoring time point, represents the reference position variation parameter, Indicates the allowable deviation position change parameter, represents the motion angle variation parameter at the i-th monitoring time point, represents the reference motion angle variation parameter, Indicates the allowable deviation motion angle variation parameter, represents the motion rate variation parameter at the i-th monitoring time point, represents the reference motion rate variation parameter, Indicates the allowable deviation motion rate change parameter, represents the equipment performance index at the ith monitoring time point, represents the critical equipment performance index, represents the site environmental assessment value at the ith monitoring time point, Represents the critical site environmental assessment value.

[0058] In this embodiment, , , , and The image accuracy assessment influencing factors corresponding to the basketball position, basketball movement angle, basketball movement speed, equipment performance index and venue environment assessment value preset in the basketball detection database are respectively, and these influencing factors are numerical indicators that measure the influence of the above parameters on the image accuracy assessment value. Specifically, there is a mapping relationship table for each basketball position, basketball movement angle, basketball movement speed, equipment performance index and venue environment assessment value, and the table records each possible parameter value and its corresponding image accuracy assessment influencing factor, and these mapping relationships can be one-to-one or many-to-one. In actual applications, when it is necessary to evaluate the image accuracy assessment value at a certain monitoring time point, the measured basketball position, basketball movement angle, basketball movement speed, equipment performance index and venue environment assessment value can be respectively input into the corresponding mapping relationship table, and the image accuracy assessment influencing factors corresponding to these values ​​can be quickly found, where the value range of the influencing factor is between 0 and 1.

[0059] Furthermore, the step of adjusting feedback according to the image accuracy evaluation value includes: obtaining an image accuracy evaluation threshold from a basketball detection database; comparing the image accuracy evaluation value with the image accuracy evaluation threshold, if the image accuracy evaluation value is less than the image accuracy evaluation threshold, further performing image correction processing on the corrected basketball image, if the image accuracy evaluation value is greater than or equal to the image accuracy evaluation threshold, no additional processing is performed.

[0060] In this embodiment, the image accuracy assessment threshold is a set value used to determine whether the current image accuracy assessment value meets the quality standard. If the image accuracy assessment value is less than the image accuracy assessment threshold, it means that the accuracy of the current image is low, and the corrected basketball image needs to be further processed to improve the image quality; if the image accuracy assessment value is greater than or equal to the image accuracy assessment threshold, it means that the accuracy of the current image has met the requirements and no additional processing is required. When performing image correction processing on the corrected basketball image, manual annotation can be introduced to assist in identifying and correcting the position and motion state of the basketball in the image or adjusting the parameters of the target tracking and recognition algorithm, such as the detection threshold, tracking window size, etc., to improve the accuracy and stability of the algorithm.

[0061] like Figure 3 As shown, it is a structural schematic diagram of an artificial intelligence-based basketball detection system provided in an embodiment of the present application. The artificial intelligence-based basketball detection system provided in an embodiment of the present application includes: a data acquisition module, an image correction module, an image adjustment module and a basketball detection database; wherein the data acquisition module is used to acquire venue environment data, equipment performance data and basketball image data; the image correction module is used to obtain the venue environment evaluation value of each monitoring time point according to the comprehensive analysis of the venue environment data, and adjust the basketball image data according to the venue environment evaluation value of each monitoring time point to obtain the corrected basketball image of each monitoring time point; the image adjustment module is used to obtain the equipment performance index of each monitoring time point according to the comprehensive analysis of the equipment performance data, obtain the image accuracy evaluation value of each monitoring time point according to the comprehensive analysis of the corrected basketball image of each monitoring time point, the equipment performance index of each monitoring time point and the venue environment evaluation value of each monitoring time point, and perform adjustment feedback according to the image accuracy evaluation value.

[0062] An embodiment of the present application also provides an artificial intelligence-based basketball detection electronic device, comprising: a processor and a memory for storing instructions executable by the processor; when the processor is configured to execute the instructions, the electronic device implements an artificial intelligence-based basketball detection method.

[0063] In summary, the embodiments of the present application obtain site environment data, equipment performance data and basketball image data; obtain the site environment assessment value at each monitoring time point according to a comprehensive analysis of the site environment data, and adjust the basketball image data according to the site environment assessment value at each monitoring time point to obtain a corrected basketball image at each monitoring time point; obtain the equipment performance index at each monitoring time point according to a comprehensive analysis of the equipment performance data, obtain the image accuracy assessment value at each monitoring time point according to a comprehensive analysis of the corrected basketball image at each monitoring time point, the equipment performance index at each monitoring time point and the site environment assessment value at each monitoring time point, and perform adjustment feedback according to the image accuracy assessment value, thereby improving the image calibration accuracy and detection accuracy.

[0064] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0065] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0066] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0067] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0068] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0069] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A basketball detection method based on artificial intelligence, characterized in that: The following steps are involved: Obtain venue environment data, equipment performance data, and basketball image data; The site environment evaluation value at each monitoring time point is obtained based on the comprehensive analysis of the site environment data, and the basketball image data is adjusted based on the site environment evaluation value at each monitoring time point to obtain a corrected basketball image at each monitoring time point; The equipment performance index at each monitoring time point is obtained through comprehensive analysis of the equipment performance data. The image accuracy assessment value at each monitoring time point is obtained through comprehensive analysis of the corrected basketball image at each monitoring time point, the equipment performance index at each monitoring time point, and the site environment assessment value at each monitoring time point. Adjustment feedback is performed based on the image accuracy assessment value.

2. The basketball detection method based on artificial intelligence as claimed in claim 1, characterized in that: The step of obtaining the site environment assessment value at each monitoring time point based on comprehensive analysis of the site environment data comprises: The site environment data includes light intensity, illumination data and light coverage, wherein the illumination data includes vertical illumination and horizontal illumination; Monitor the vertical and horizontal illumination at each location, and obtain the reference illumination intensity, allowable deviation illumination intensity, allowable deviation vertical illumination, allowable deviation horizontal illumination and critical illumination coverage from the basketball detection database; Comprehensive analysis was performed to obtain the site environmental assessment value at each monitoring time point.

3. The basketball detection method based on artificial intelligence as claimed in claim 1, characterized in that: The step of adjusting the basketball image data according to the field environment evaluation value at each monitoring time point to obtain a corrected basketball image at each monitoring time point comprises: The basketball image data includes basketball images at each monitoring time point; Obtaining a site environment assessment threshold from a basketball detection database, comparing the site environment assessment value at each monitoring time point with the site environment assessment threshold, and if the site environment assessment value at a certain monitoring time point is less than the site environment assessment threshold, subtracting the site environment assessment threshold from the site environment assessment value at the monitoring time point to obtain a deviation site environment assessment value, and adjusting the basketball image at the monitoring time point according to the deviation site environment assessment value to obtain a corrected basketball image at the monitoring time point; If the site environmental assessment value at a certain monitoring time point is greater than or equal to the site environmental assessment threshold, no additional processing is performed; The corrected basketball images at each monitoring time point were obtained by statistics.

4. The basketball detection method based on artificial intelligence as claimed in claim 3, characterized in that: The step of adjusting the basketball image at the monitoring time point according to the deviation field environment assessment value comprises: The deviation venue environment assessment value is matched with the contrast adjustment amplitude level corresponding to each deviation venue environment assessment value preset in the basketball detection database to obtain the basketball image contrast adjustment amplitude level, and the contrast of the basketball image is adjusted according to the basketball image contrast adjustment amplitude level.

5. The basketball detection method based on artificial intelligence as claimed in claim 1, characterized in that: The step of obtaining the equipment performance index at each monitoring time point based on comprehensive analysis of the equipment performance data includes: Device performance data, including battery level, resolution, and frame rate; Obtaining critical battery power, critical resolution, reference frame rate, and allowable deviation frame rate from a basketball detection database; The equipment performance index at each monitoring time point is obtained through comprehensive analysis.

6. The basketball detection method based on artificial intelligence as claimed in claim 1, characterized in that: The step of comprehensively analyzing the corrected basketball image at each monitoring time point, the equipment performance index at each monitoring time point, and the site environment evaluation value at each monitoring time point to obtain the image accuracy evaluation value at each monitoring time point comprises: Extract the basketball position, basketball movement angle and basketball movement speed at each monitoring time point from the corrected basketball images at each monitoring time point, and mark them together as basketball movement data at each monitoring time point; The basketball motion data at each monitoring time point are compared with the basketball motion data at the adjacent previous monitoring time point to obtain the position change parameter, motion angle change parameter and motion speed change parameter at each monitoring time point; According to the basketball movement rate matching at each monitoring time point, the reference position change parameter and the reference movement angle change parameter at each monitoring time point are obtained, and the allowable deviation position change parameter, the allowable deviation movement angle change parameter, the allowable deviation movement rate change parameter, the reference movement rate change parameter, the critical equipment performance index and the critical site environment assessment value are obtained from the basketball detection database; The image accuracy assessment value at each monitoring time point was obtained through comprehensive analysis.

7. The basketball detection method based on artificial intelligence as claimed in claim 6, characterized in that: The method for obtaining the image accuracy evaluation value at each monitoring time point is as follows: ； In the formula, represents the image accuracy evaluation value at the i-th monitoring time point, Indicates the impact factor of image accuracy evaluation corresponding to the basketball position, Indicates the impact factor of image accuracy evaluation corresponding to basketball motion angle, Indicates the impact factor of image accuracy evaluation corresponding to basketball movement speed, Indicates the image accuracy assessment impact factor corresponding to the device performance index, Indicates the image accuracy assessment impact factor corresponding to the site environment assessment value, represents the position change parameter at the i-th monitoring time point, represents the reference position variation parameter, Indicates the allowable deviation position change parameter, represents the motion angle variation parameter at the i-th monitoring time point, represents the reference motion angle variation parameter, Indicates the allowable deviation motion angle variation parameter, represents the motion rate variation parameter at the i-th monitoring time point, represents the reference motion rate variation parameter, Indicates the allowable deviation motion rate change parameter, represents the equipment performance index at the ith monitoring time point, represents the critical equipment performance index, represents the site environmental assessment value at the ith monitoring time point, Represents the critical site environmental assessment value, where i is the number of each monitoring time point, i=1, 2, 3, ..., N, and N is the total number of monitoring time points.

8. The basketball detection method based on artificial intelligence as claimed in claim 1, characterized in that: The step of performing adjustment feedback according to the image accuracy evaluation value comprises: Get image accuracy assessment thresholds from the basketball detection database; The image accuracy assessment value is compared with the image accuracy assessment threshold. If the image accuracy assessment value is less than the image accuracy assessment threshold, the corrected basketball image is further subjected to image correction processing. If the image accuracy assessment value is greater than or equal to the image accuracy assessment threshold, no additional processing is performed.

9. An artificial intelligence-based basketball detection system, using an artificial intelligence-based basketball detection method as claimed in any one of claims 1 to 8, characterized in that: It includes a data acquisition module, an image correction module, an image adjustment module and a basketball detection database; Wherein, the data acquisition module is used to acquire venue environment data, equipment performance data and basketball image data; The image correction module is used to obtain the site environment evaluation value at each monitoring time point according to the comprehensive analysis of the site environment data, and adjust the basketball image data according to the site environment evaluation value at each monitoring time point to obtain the corrected basketball image at each monitoring time point; The image adjustment module is used to obtain the equipment performance index at each monitoring time point based on a comprehensive analysis of the equipment performance data, obtain the image accuracy evaluation value at each monitoring time point based on a comprehensive analysis of the corrected basketball image at each monitoring time point, the equipment performance index at each monitoring time point and the venue environment evaluation value at each monitoring time point, and perform adjustment feedback based on the image accuracy evaluation value.

10. An artificial intelligence-based basketball detection electronic device, characterized in that: include: a processor, a memory for storing instructions executable by the processor; When the processor is configured to execute the instructions, the electronic device implements the basketball detection method based on artificial intelligence as described in any one of claims 1-8.

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