Intelligent karst groundwater exploration image processing system

Abstract

The invention discloses an intelligent karst groundwater exploration image processing system. The system comprises an image acquisition module, a geologic body gray level layering enhancement module, a geologic feature stretching module, a karst boundary restoration module, a damage track positioning optimization module, a karst damage track identification model establishment module and a karst groundwater exploration image processing module. The invention belongs to the field of image processing, and particularly relates to an intelligent karst groundwater exploration image processing system, which is characterized in that image gray scale is partitioned, and a attribution degree function is designed to realize directional enhancement guided by geological knowledge; a dynamic hazy rule base is established based on a local window background mean value, and shallow weak anomaly is prevented from being masked; the gray scale stretching slope of the extremely low gray scale region is improved based on the adoption of a dark region enhanced power state spreading function, and weak signals are accurately captured; designing a corner and parietal constraint loss function, accurately matching the irregular contour, and reducing the influence of slight offset of the marking frame; and the karst loss identification accuracy of the karst groundwater exploration image is improved.

Description

An intelligent image processing system for karst groundwater exploration Technical Field

[0001] This invention relates to the field of image processing, specifically to an intelligent image processing system for karst groundwater exploration. Background Technology

[0002] Karst groundwater exploration image processing systems refer to specialized technical systems that utilize computer vision technology to preprocess, enhance, extract features, and identify damage in raw exploration images obtained from high-density electrical resistivity tomography, ground-penetrating radar, and borehole imaging. This enables accurate detection and analysis of water-rich anomalies in karst caves and fissures. However, general karst groundwater exploration image processing systems often suffer from problems such as ignoring the physical properties of karst areas, failing to highlight deep water-bearing anomalies, and failing to capture weak signals from water-rich fissures, resulting in poor image processing performance. Furthermore, these systems often amplify noise, produce jagged artifacts at fissure edges, and experience contour matching deviations due to corner point misalignment, leading to poor accuracy in karst damage identification. Summary of the Invention

[0003] To address the above issues and overcome the shortcomings of existing technologies, this invention provides an intelligent image processing system for karst groundwater exploration. Addressing the problems of general karst groundwater exploration image processing systems neglecting the physical properties of karst areas, failing to highlight deep water-bearing anomalies, and failing to capture weak signals from water-rich fractures, resulting in poor image processing performance, this solution divides image grayscale into low-resistivity, transition, and high-resistivity zones based on the physical properties of the karst area. It also designs a membership function to achieve geologically guided directional enhancement. A dynamic blurring rule library is established based on the local window background mean, ensuring accurate adaptation of low-resistivity dark enhancement to the local background and preventing the obscuring of shallow weak anomalies. Furthermore, it employs a power-law enhancement method for dark areas. The expansion function increases the grayscale stretching slope in extremely low grayscale areas deep within karst caves and inside pipes, accurately capturing weak signals; thus improving the image processing effect of karst groundwater exploration. Addressing the issues of amplified noise, jagged artifacts at fissure edges, and contour matching deviations due to corner misalignment in general karst groundwater exploration image processing systems, leading to poor accuracy in karst damage identification, this solution uses Soxhlet gradient sharpening to precisely enhance edges, only enhancing the edge response of geological boundaries to avoid noise interference in non-boundary areas; a corner-end constraint loss function is designed to accurately match irregular contours and reduce the impact of slight offsets in the annotation box; thereby improving the accuracy of karst damage identification in karst groundwater exploration images.

[0004] The technical solution adopted by this invention is as follows: This invention provides an intelligent karst groundwater exploration image processing system, including an image acquisition module, a geological body grayscale layering enhancement module, a geological feature stretching module, a karst boundary repair module, a damage location optimization module, a karst damage identification model establishment module, and a karst groundwater exploration image processing module; the image acquisition module acquires historical karst exploration images, and constructs a karst exploration image set after standardized preprocessing; the geological body grayscale layering enhancement module adjusts the grayscale of the karst exploration image set through a dynamic haze rule library and centroid method to achieve geological knowledge-guided enhancement; the geological feature stretching module enhances the grayscale of the geological body after grayscale layering... The karst exploration image undergoes cropping, constraint equalization, and power-law tiling to enhance dark areas, thus stretching geological features. The karst boundary repair module calculates the edge response of the stretched karst exploration image and repairs the karst boundary through kurtosis overlay and truncation processing to obtain the final enhanced image. The damage location optimization module designs a corner-peak constraint loss function that integrates overlap, center point, and corner-peak distance. The karst damage identification model establishment module establishes a karst damage identification model based on the karst exploration image after karst boundary repair and the corner-peak constraint loss function. The karst groundwater exploration image processing module processes real-time karst exploration images based on the karst damage identification model.

[0005] Furthermore, the image acquisition module acquires historical karst exploration images, performs image standardization preprocessing and image annotation, and constructs a karst exploration image set. The image standardization preprocessing includes: scale unification, resampling to a uniform spatial resolution, and using center cropping to adjust all images to the standard size of the model input; grayscale normalization, converting color images into grayscale images; the image annotation uses an annotation tool to draw a rectangular box around the target on the image and specify the damage category label.

[0006] Furthermore, the geological body grayscale layer enhancement module, for the karst exploration image set, divides the grayscale of the karst exploration image into three geological layers—low resistivity zone, transition zone, and high resistivity zone—based on the physical characteristics of the karst area; and performs targeted enhancement on each layer, strengthening the darkness of the low resistivity zone, maintaining the grayscale of the transition zone, and increasing the brightness of the high resistivity zone, thereby achieving pre-enhancement that adds geological knowledge guidance to the image; specifically, it defines three attribution functions, establishes a dynamic haze rule library, modifies the attribution of each pixel, and finally uses the centroid method to calculate the determined output grayscale value.

[0007] Furthermore, the geological feature stretching module divides the karst exploration image set after layered enhancement into overlapping geological units, and performs two-step processing within each unit: Step 1, equalization of the histogram within the unit with cropping restrictions, while preventing excessive amplification of noise through cropping restrictions; Step 2, based on the initial equalization, redistributing gray levels using a dark area enhancement power-law spreading function; the function introduces dark area enhancement sigmoid modulation to increase the slope of gray level stretching in extremely low gray level regions.

[0008] Furthermore, the karst boundary repair module uses the Soxhlet gradient sharpening property to enhance areas of abrupt grayscale changes in karst exploration images after geological feature stretching, resulting in a final enhanced image.

[0009] Furthermore, the damage localization optimization module designs a corner-peak constraint loss function that simultaneously considers overlapping areas, center point distance, and corner-peak distance to minimize the distance between the corresponding corners of the predicted bounding box and the ground truth bounding box.

[0010] Furthermore, the karst damage identification model building module takes the final enhanced image as input and constructs a karst damage identification model based on the vertex constraint loss function. The model adopts a backbone-neck-head architecture. The input is the final enhanced karst exploration image. The output is the category, confidence level, and bounding box coordinates of all karst exploration images in the image. The backbone network adopts a lightweight network constructed with depthwise separable convolution. The neck network adopts a weighted bidirectional feature pyramid network, which fuses and optimizes multi-scale features from the backbone network. The detection head adopts a decoupled head, using two independent branches to handle classification and regression tasks respectively.

[0011] Furthermore, the karst groundwater exploration image processing module acquires karst exploration images in real time, and after preprocessing, performs geological body grayscale layer enhancement, geological feature stretching, and karst boundary repair. Finally, the processed karst exploration images are sent to the karst damage recognition model, which outputs the type, confidence level, and bounding box of caves and fissures in real time, realizing on-site instant detection and feedback.

[0012] The beneficial effects of the present invention obtained by adopting the above scheme are as follows: (1) In view of the problem that the general karst groundwater exploration image processing system ignores the physical characteristics of karst areas, makes it difficult to highlight deep water-bearing anomalies, and fails to capture weak signals of water-rich fissures, thus resulting in poor exploration image processing effect, this scheme divides the image gray level into low resistivity area, transition area and high resistivity area according to the physical properties of karst area, and designs a degree function to realize the directional enhancement guided by geological knowledge; establishes a dynamic hazy rule library based on the local window background mean, so that the dark enhancement of low resistivity area is accurately adapted to the local background, and avoids the shallow weak anomaly being covered; based on the use of dark area enhancement power-state spreading function to increase the gray level stretching slope of the extremely low gray level area in the depth of the cave and inside the pipe, accurately captures weak signals; thereby improving the karst groundwater exploration image processing effect.

[0013] (2) To address the problems of amplified noise in general karst groundwater exploration image processing systems, resulting in jagged artifacts at the edges of fissures and contour matching deviations due to corner misalignment, which leads to poor accuracy in karst damage identification, this solution uses Soxhlet gradient sharpening to precisely enhance the edges, only enhancing the edge response of geological boundaries to avoid noise interference in non-boundary areas; it designs a corner-peak constraint loss function to accurately match irregular contours and reduce the impact of slight offset of the annotation box; thereby improving the accuracy of karst damage identification in karst groundwater exploration images. Attached Figure Description

[0014] Figure 1 is a flowchart of an intelligent karst groundwater exploration image processing system provided by the present invention.

[0015] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. Detailed Implementation

[0016] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0017] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0018] Example 1, referring to Figure 1, provides an intelligent karst groundwater exploration image processing system, including an image acquisition module, a geological body grayscale layering enhancement module, a geological feature stretching module, a karst boundary repair module, a damage location optimization module, a karst damage identification model establishment module, and a karst groundwater exploration image processing module. The image acquisition module acquires historical karst exploration images, performs standardized preprocessing to construct a karst exploration image set, and sends the data to the geological body grayscale layering enhancement module. The geological body grayscale layering enhancement module adjusts the grayscale of the karst exploration image set using a dynamic haze rule base and centroid method to achieve geological knowledge-guided enhancement. The data is then sent to the geological feature stretching module. The geological feature stretching module performs cropping, limiting equalization, and dark area enhancement on the karst exploration images after geological body grayscale layering. The data is stretched using a power-law approach to achieve geological feature extrusion. The data is then sent to a karst boundary repair module. This module calculates the edge response of the stretched karst exploration image and repairs the karst boundary through kurtosis overlay and truncation processing to obtain the final enhanced image. The data is then sent to a damage location optimization module, which designs a vertex constraint loss function that integrates overlap, center point, and vertex distance. The data is then sent to a karst damage identification model building module, which establishes a karst damage identification model based on the karst exploration image after boundary repair and the vertex constraint loss function. Finally, the data is sent to a karst groundwater exploration image processing module, which processes the real-time karst exploration image based on the karst damage identification model.

[0019] Example 2, referring to Figure 1, is based on the above example. The image acquisition module acquires historical karst exploration images, performs image standardization preprocessing and image annotation, and constructs a karst exploration image set. The historical karst exploration images include high-density electrical resistivity cross-section maps, ground-penetrating radar profile maps, and borehole imaging maps. Image standardization preprocessing includes: scale unification, resampling to a uniform spatial resolution (1 pixel = 0.1 meters), and using center cropping to adjust all images to the standard size of the model input (640x640 pixels); grayscale normalization, converting color images to grayscale images. Image annotation uses an annotation tool (LabelImg) to draw a rectangular box around the target on the image and specify the damage category label (cavity, fissure, dissolution conduit, weathering crust). All annotations must undergo a cross-review process: initial annotation by junior engineers → review by senior engineers → expert group to judge disputed samples to ensure the accuracy and consistency of annotations.

[0020] Example 3, referring to Figure 1, is based on the above examples. The geological body grayscale layering enhancement module, for a karst exploration image set, divides the grayscale of the karst exploration image into three geological layers based on the physical characteristics of the karst area: a low-resistivity zone (water-rich target), a transition zone (lithological interface), and a high-resistivity zone (intact surrounding rock). It then performs targeted enhancement on each layer, strengthening the darkness of the low-resistivity zone to highlight water-bearing anomalies, maintaining the grayscale of the transition zone to preserve the lithological interface, and appropriately increasing the brightness of the high-resistivity zone to distinguish the surrounding rock. This achieves a pre-enhancement layer guided by geological knowledge, highlighting water-bearing anomalies and preserving the lithological interface. Specifically, it defines three membership functions. , and , representing the degree to which the pixel grayscale value g belongs to the low, medium, and high resistivity regions, respectively. The function for classifying the pixel as belonging to the low resistivity region is expressed as: The transition zone affiliation function is expressed as: The high-resistivity region affiliation function is expressed as: g is the pixel grayscale value, and a, b, c, and d are function parameters that control the grayscale range of the low-resistivity region. a takes the first 20% of the histogram cumulative spread, b takes the first 35%, c takes the first 55%, and d takes the first 80%. , and These represent the confidence levels of a pixel belonging to the low-resistivity, medium-resistivity, and high-resistivity regions, respectively. A dynamic haze rule base is established to modify the classification of each pixel. Finally, the centroid method is used to calculate the determined output grayscale value, which is expressed as: ; It is the output belonging degree after the i-th vague rule is activated. ; It represents the output grayscale center value of the corresponding rule; N is the total number of hazy rules; These are the grayscale values ​​of the image pixels after layered enhancement; This is the average background value of a local window, with a window size of 5×5. It is the initial degree of belonging; It is a modulation function. ; It is the local background modulation coefficient, which is 0.2 in the low impedance region, 0.1 in the transition region, and 0.3 in the high impedance region; and These are the minimum and maximum grayscale values ​​of the image, respectively. A dynamic blurring rule base is established: Rule 1: If a pixel belongs to a low-resistivity area, reduce its grayscale value; Rule 2: If a pixel belongs to a transition area, keep its grayscale value unchanged; Rule 3: If a pixel belongs to a high-resistivity area, enhance its grayscale value; The dynamic center value is automatically adjusted based on the local background mean to make the dark enhancement of water-rich targets more accurate; and an output grayscale center value is specified for each rule. ; ; , and These are the grayscale values ​​after the pixel belongs to the low-resistivity region, the transition region, and the high-resistivity region, respectively. and These are empirical coefficients, all with values ​​of (0.5, 0.8); m and n are neighborhood offset indices; It is the gray value of the image at the corresponding coordinates.

[0021] Example 4, referring to Figure 1, is based on the above examples. The geological feature stretching module divides the enhanced karst exploration image set into overlapping geological units. Within each unit, a two-step processing is performed: Step 1, equalization with cropping constraints on the histogram within the unit. This safely widens the overall grayscale dynamic range of the local area, initially improving the visual separability between the surrounding rock and the anomalous area. Simultaneously, cropping constraints prevent noise from being excessively amplified into false anomalies. Step 2, based on the initial equalization, a dark area enhancement power-law spreading function is used to redistribute the grayscale levels. The function introduces dark area enhancement sigmoid modulation, which can increase the slope of grayscale stretching in extremely low grayscale areas, thereby extremely finely depicting grayscale changes deep within karst caves and inside pipes, and providing higher sensitivity to weak signals from water-rich fissures. The overall representation is as follows: ;in, F is a stretched karst exploration image showing geological features; F is a layered enhanced karst exploration image. It is the first output step; It is a step-by-step output; average number of pixels per block. for ; This is the block size, using 16×16; L is the number of gray levels within the block, 256; the maximum number of pixels allowed per gray level, CL, is expressed as: ; This is the clipping factor, ranging from 0.2 to 0.5; dark area enhancement power-state spreading mapping function. Represented as: ; ; It is the cumulative spread probability; It is a power factor. The larger the value, the stronger the stretching of low grayscale areas. The value ranges from 2 to 5. Histogram cropping and redistribution: For the histogram of each block, the part that exceeds CL is cropped, and the total amount of cropping is evenly added back to all grayscale levels to ensure that the total number of pixels remains unchanged and to avoid grayscale loss. It is a dark area enhancement function; This is the dark area threshold, set to 0.2L; Adjust the slope, set to 10-20; It is the intensity enhancement coefficient, with a value of 0.5 to 1.0, which controls the magnitude of the enhancement in dark area response.

[0022] By performing the above operations, this solution addresses the problems of general karst groundwater exploration image processing systems, which neglect the physical characteristics of karst areas, fail to highlight deep water-bearing anomalies, and cannot capture weak signals from water-rich fissures, resulting in poor image processing performance. Based on the physical properties of karst areas, this solution divides image grayscale into low-resistivity, transition, and high-resistivity zones, and designs a membership function to achieve geologically guided directional enhancement. A dynamic blurring rule library is established based on the local window background mean, ensuring accurate adaptation of low-resistivity dark enhancement to the local background and preventing shallow weak anomalies from being masked. Furthermore, by employing a dark area enhancement power-state spreading function, the grayscale stretching slope is increased in extremely low grayscale areas deep within karst caves and inside pipes, accurately capturing weak signals. This ultimately improves the image processing performance of karst groundwater exploration.

[0023] Example 5, referring to Figure 1, is based on the above examples. The karst boundary repair module addresses the issue that after strong contrast stretching, karst boundaries (cavity walls, fracture surfaces) in the image become blurry or jagged. Therefore, for stretched karst exploration images, the Soxhlet gradient sharpening feature (extremely sensitive to areas of high variation in the first derivative of grayscale (i.e., edges)) is utilized to precisely enhance areas of abrupt grayscale changes, restoring the clear and sharp morphology of cavity walls and fracture surfaces, making the geometric shape of the geological structure clearer. The Soxhlet gradient sharpening output... Represented as: ; ; and These are the convolution responses in the horizontal and vertical directions, respectively; the final kurtosis output is represented as: ; It ultimately enhances the image pixels; It is the kurtosis intensity coefficient, used to control the magnitude of edge enhancement, with a value ranging from 0.1 to 0.5; It is a truncation process to ensure that the pixel value is within the range of [0, 255].

[0024] Example 6, referring to Figure 1, is based on the above examples. The damage localization optimization module designs a corner-peak constraint loss function for noise annotations of karst caves and fissures in the image. It considers overlapping areas, center point distance, and corner-peak distance to explicitly minimize the distance between the predicted bounding box and the corresponding corner-peak of the ground truth bounding box. For karst damage, especially long fissures and irregular caves, it can accurately match the four corner points of the contour. This precisely guides the network to adjust the predicted bounding box, aligning the corner-peaks with the corner points of the actual geological damage, thus providing stronger robustness to noise annotations. Let the coordinates of the ground truth bounding box A be... The coordinates of the predicted bounding box B are The image width and height are w and h; construct a vertex distance penalty term: calculate the normalized distance between the top left and bottom right vertices of the two bounding boxes. and , is represented as: ; Total loss Represented as: ; It is intersection, union, and ratio; It is the square of the Euclidean distance between the center points of frame A and frame B; Constraints are integrated for overlap, center point distance, and corner vertex distance; It is the distance penalty weight, with a value ranging from 0.5 to 1.

[0025] Example 7, referring to Figure 1, is based on the above examples. The karst damage identification model building module takes the final enhanced image as input and constructs a karst damage identification model based on the vertex constraint loss function. The model adopts a backbone-neck-head architecture. The input is the final enhanced karst exploration image. The output is the category, confidence level, and bounding box coordinates of all karst exploration images in the image. The backbone network uses a lightweight network (MobileNetV3) constructed with depthwise separable convolutions to progressively downsample the image from shallow to deep layers, extracting feature maps of different scales (1 / 8, 1 / 16, 1 / 32 feet). The core network employs a weighted bidirectional feature pyramid network, fusing and optimizing multi-scale features from the backbone network. Learnable weights dynamically adjust the contribution of each scale feature, enhancing multi-scale damage detection capabilities. The detection head uses a decoupled head, with two independent branches handling classification (damage category) and regression (boundary box coordinates) tasks respectively. Regarding model training: the training objective is to minimize the total loss, consisting of three parts: classification loss (using FocalLoss); regression loss (using vertex constraint loss to optimize bounding box position); and target... Loss: A binary cross-entropy loss is used to determine whether the anchor box contains the target; the total loss is a weighted sum of the three factors, with the weights initially set at a ratio of 1.0:5.0:1.0 and then adjusted based on model performance; the final enhanced karst exploration image set is randomly divided into training, validation, and test sets in a 7:2:1 ratio; the training set is used for model parameter learning, the validation set is used to monitor the training process, adjust hyperparameters, and select the best model, and the test set is used to finally evaluate the model's generalization performance; the AdamW optimizer is used for parameter tuning, and all possible values ​​in the model are updated in reverse based on the calculated total loss gradient. Training parameters; learning rate scheduling based on cosine annealing strategy; after each training epoch, evaluate model performance (average precision, recall) on the validation set. If the validation set performance no longer improves over several consecutive epochs (improvement threshold 0.001), stop training early; use a test set that has never been used in the training sequence to perform a final evaluation on the saved best model. If the model performance meets the standard (average precision reaches 85% at an IoU threshold of 0.5, and recall for the blemish category is not less than 80%), the model training is complete; otherwise, adjust the model initialization parameters and retrain.

[0026] By performing the above operations, this solution addresses the problems of amplified noise, jagged artifacts at fracture edges, and contour matching deviations caused by corner misalignment in general karst groundwater exploration image processing systems, which lead to poor accuracy in karst damage identification. This solution uses Soxhlet gradient sharpening to precisely enhance edges, only strengthening the edge response of geological boundaries to avoid noise interference in non-boundary areas. A corner-end constraint loss function is designed to accurately match irregular contours and reduce the impact of slight offsets in the annotation box. This improves the accuracy of karst damage identification in karst groundwater exploration images.

[0027] Example 8, referring to Figure 1, is based on the above examples. The karst groundwater exploration image processing module acquires karst exploration images in real time. After preprocessing, it performs grayscale layer enhancement of geological bodies, stretching of geological features, and repair of karst boundaries. Finally, the processed karst exploration images are sent to the karst damage recognition model, which outputs the type, confidence level, and bounding box of caves and fissures in real time, realizing on-site instant detection and feedback.

[0028] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

[0029] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. An intelligent image processing system for karst groundwater exploration, characterized in that: The system includes an image acquisition module, a geological body grayscale layering enhancement module, a geological feature stretching module, a karst boundary repair module, a damage location optimization module, a karst damage identification model establishment module, and a karst groundwater exploration image processing module. The image acquisition module acquires historical karst exploration images, performs standardized preprocessing, and constructs a karst exploration image set. The geological body grayscale layering enhancement module adjusts the grayscale of the karst exploration image set using a dynamic haze rule library and centroid method to achieve geological knowledge-guided enhancement. The geological feature stretching module performs cropping, limiting equalization, and dark area enhancement power-law spreading on the grayscale-layered karst exploration images to achieve geological feature stretching. The karst boundary repair module calculates the edge response of the stretched karst exploration image based on geological features, and repairs the karst boundary through kurtosis overlay and truncation processing to obtain the final enhanced image; the damage location optimization module designs a corner-peak constraint loss function that integrates overlap, center point, and corner-peak distance; the karst damage identification model establishment module establishes a karst damage identification model based on the karst exploration image after karst boundary repair and the corner-peak constraint loss function; the karst groundwater exploration image processing module processes real-time karst exploration images based on the karst damage identification model.

2. The intelligent karst groundwater exploration image processing system according to claim 1, characterized in that: The geological body grayscale layer enhancement module divides the grayscale of karst exploration images into three geological layers—low resistivity zone, transition zone, and high resistivity zone—based on the physical properties of the karst area. It then performs targeted enhancement on each layer, strengthening the darkness of the low resistivity zone, maintaining the grayscale of the transition zone, and increasing the brightness of the high resistivity zone, thereby achieving pre-enhancement that adds geological knowledge guidance to the images. Specifically, three affiliation functions are defined, a dynamic haze rule base is established, the affiliation of each pixel is modified, and finally the centroid method is used to calculate the determined output grayscale value.

3. The intelligent karst groundwater exploration image processing system according to claim 1, characterized in that: The geological feature stretching module divides the karst exploration image set after layered enhancement into overlapping geological units. Within each unit, it performs a two-step process: Step 1, it performs equalization on the histogram within the unit with cropping constraints, while preventing excessive amplification of noise through cropping constraints; Step 2, based on the initial equalization, it redistributes gray levels using a dark area enhancement power-law spreading function; the function introduces dark area enhancement sigmoid modulation to increase the slope of gray level stretching in extremely low gray level regions.

4. The intelligent karst groundwater exploration image processing system according to claim 1, characterized in that: The karst boundary repair module uses Soxhlet gradient sharpening to enhance areas of abrupt grayscale changes in karst exploration images after geological feature stretching, resulting in a final enhanced image.

5. The intelligent karst groundwater exploration image processing system according to claim 1, characterized in that: The damage localization optimization module designs a corner-peak constraint loss function that simultaneously considers overlapping areas, center point distance, and corner-peak distance to minimize the distance between the corresponding corner-peaks of the predicted bounding box and the ground truth bounding box.

6. The intelligent karst groundwater exploration image processing system according to claim 1, characterized in that: The karst damage identification model building module takes the final enhanced image as input and constructs a karst damage identification model based on the vertex constraint loss function; the model adopts a backbone-neck-head architecture; The input is the final enhanced karst exploration image; the output is the category, confidence level, and bounding box coordinates of all karst exploration images in the image; the backbone network is a lightweight network constructed using depthwise separable convolutions; the neck network is a weighted bidirectional feature pyramid network that fuses and optimizes multi-scale features from the backbone network. The detection head uses a decoupled head, employing two independent branches to handle classification and regression tasks respectively.

7. The intelligent karst groundwater exploration image processing system according to claim 1, characterized in that: The image acquisition module acquires historical karst exploration images, performs image standardization preprocessing and image annotation, and constructs a karst exploration image set. Image normalization preprocessing includes: scale unification, resampling to a uniform spatial resolution, and center cropping to adjust all images to the standard size of the model input; Grayscale normalization converts a color image into a grayscale image; image annotation involves using an annotation tool to draw a rectangular box around the target on the image and specifying a damage category label.

8. The intelligent karst groundwater exploration image processing system according to claim 1, characterized in that: The karst groundwater exploration image processing module acquires karst exploration images in real time. After preprocessing, it performs grayscale layer enhancement of geological bodies, stretching of geological features, and repair of karst boundaries. Finally, the processed karst exploration images are sent to the karst damage recognition model, which outputs the type, confidence level, and bounding box of caves and fissures in real time, enabling real-time on-site detection and feedback.

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