Urban road rain sewage well lid safety monitoring method and system

Abstract

The invention discloses an urban road rain sewage well lid safety monitoring method and system, and relates to the technical field of urban infrastructure monitoring, and the method comprises the steps: collecting an image of an urban road rain sewage well lid; the collected images are preprocessed; an image recognition model is constructed to recognize the preprocessed image, and the position state of the well lid is recognized; comparing and judging the identified position state of the well lid with a preset standard state; and generating alarm information according to a judgment result. According to the invention, an unmanned aerial vehicle is utilized to carry a high-definition camera to carry out image acquisition on rain sewage well lids on urban roads, and the image is identified through a deep learning algorithm to judge whether the well lids are lost or shifted. Once an abnormal condition is detected, the system automatically generates alarm information and sends the alarm information to a related management department and an emergency response team through a wireless communication network. The manhole cover can be monitored in real time, the monitoring efficiency is improved, and the safety of pedestrians and vehicles is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of urban infrastructure monitoring, and more particularly to a method and system for monitoring the safety of urban road rainwater and sewage manhole covers. Background Art

[0002] Manhole covers on urban roads are an important part of urban infrastructure, and their safety and integrity are directly related to the safety of pedestrians and vehicles. However, due to various reasons, such as theft, vandalism, heavy rain or other natural disasters, manhole covers may be lost or displaced, which may not only cause traffic accidents, but also cause pedestrians or vehicles to fall into the well, causing serious personal injury and property loss. At present, the monitoring of manhole covers mainly relies on manual inspections, which is inefficient, costly, and difficult to achieve real-time monitoring.

[0003] Therefore, how to propose a method and system for safety monitoring of rainwater and sewage manhole covers on urban roads, and use the drone image recognition system to identify and alarm the loss or displacement of various rainwater and sewage manhole covers on urban roads is an urgent problem that technical personnel in this field need to solve. Summary of the invention

[0004] In view of this, the present invention provides a method and system for monitoring the safety of rainwater and sewage manhole covers on urban roads, which uses an unmanned aerial vehicle image recognition system to identify and alarm the loss or displacement of various rainwater and sewage manhole covers on urban roads, so as to solve the problems of low efficiency, high cost and difficulty in real-time monitoring in the prior art. In order to achieve the above purpose, the present invention adopts the following technical solutions:

[0005] A method for monitoring the safety of urban road rainwater and sewage manhole covers, comprising:

[0006] Collect images of rainwater and sewage manhole covers on urban roads;

[0007] Preprocess the collected images;

[0008] Construct an image recognition model to recognize the preprocessed image and identify the position status of the manhole cover;

[0009] Compare and judge the position status of the identified manhole cover with the preset standard status;

[0010] Generate alarm information based on the judgment result.

[0011] Optionally, the collecting of images of rainwater and sewage manhole covers on urban roads includes: utilizing a drone equipped with a high-definition camera to collect images of rainwater and sewage manhole covers on urban roads on a regular or real-time basis.

[0012] Optionally, the preprocessing of the collected image includes: denoising, contrast enhancement, image enhancement and image cropping.

[0013] Optionally, it also includes: after generating the alarm information according to the judgment result, sending the alarm information to the background through the wireless communication network.

[0014] Optionally, a database is also included to store the collected urban road rainwater and sewage manhole cover images and recognition results in the database for subsequent query and analysis.

[0015] Optionally, the database is a MySQL or MongoDB database, which is used to store image file paths, recognition results and timestamp information, and provides an API interface for querying historical data and analysis results.

[0016] Optionally, the step of constructing an image recognition model to recognize the preprocessed image and identifying the position status of the manhole cover includes:

[0017] Build an image recognition model based on the YOLOv5 model;

[0018] Collect annotated manhole cover image data, including normal manhole cover, missing manhole cover and displaced manhole cover image data, and construct annotated data sets;

[0019] Train the image recognition model based on the labeled dataset and optimize the model parameters;

[0020] The preprocessed image is input into the trained image recognition model to obtain the location and status of the manhole cover.

[0021] Optionally, comparing and judging the position status of the identified manhole cover with a preset standard status includes: defining a preset standard status, defining appearance features of a normal manhole cover, comparing the identification result with the preset standard status, and judging the abnormal type classification including: loss or displacement.

[0022] Optionally, generating alarm information according to the judgment result includes: generating alarm information including the manhole cover location, status and timestamp, sending the alarm information via SMS or email, and recording detailed information of each alarm.

[0023] Optionally, a city road rainwater and sewage manhole cover safety monitoring system comprises:

[0024] Acquisition module: used to collect images of rainwater and sewage manhole covers on urban roads;

[0025] Preprocessing module: used to preprocess the collected images;

[0026] Recognition module: used to build an image recognition model to recognize the preprocessed image and identify the position status of the manhole cover;

[0027] Comparison module: used to compare the position status of the identified manhole cover with the preset standard status;

[0028] Alarm module: used to generate alarm information based on the judgment results.

[0029] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a method and system for safety monitoring of rainwater and sewage manhole covers on urban roads, which has the following beneficial effects:

[0030] The present invention proposes a method for safety monitoring of rainwater and sewage manhole covers on urban roads, comprising: collecting images of rainwater and sewage manhole covers on urban roads; preprocessing the collected images; constructing an image recognition model to identify the preprocessed images and identify the position status of the manhole covers; comparing and judging the position status of the identified manhole covers with a preset standard status; and generating alarm information according to the judgment result. The present invention discloses a monitoring and alarm system for the loss or displacement of rainwater and sewage manhole covers on urban roads based on an unmanned aerial vehicle image recognition system, which uses a high-definition camera equipped with an unmanned aerial vehicle to collect images of rainwater and sewage manhole covers on urban roads, and recognizes the images through a deep learning algorithm to determine whether the manhole covers are lost or displaced. Once an abnormal situation is detected, the system will automatically generate an alarm message and send it to the relevant management department and emergency response team through a wireless communication network. The present invention can realize real-time monitoring of manhole covers, improve monitoring efficiency, and ensure the safety of pedestrians and vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0032] Figure 1 A schematic flow chart of a method for safety monitoring of rainwater and sewage manhole covers on urban roads provided by the present invention.

[0033] Figure 2 This is a structural framework diagram of a city road rainwater and sewage manhole cover safety monitoring system provided by the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] The embodiment of the present invention discloses a method for monitoring the safety of urban road rainwater and sewage manhole covers. Figure 1 As shown, including:

[0036] Collect images of rainwater and sewage manhole covers on urban roads;

[0037] Preprocess the collected images;

[0038] Construct an image recognition model to recognize the preprocessed image and identify the position status of the manhole cover;

[0039] Compare and judge the position status of the identified manhole cover with the preset standard status;

[0040] Generate alarm information based on the judgment result.

[0041] Furthermore, the collecting of images of rainwater and sewage manhole covers on urban roads includes: using a drone equipped with a high-definition camera to collect images of rainwater and sewage manhole covers on urban roads on a regular or real-time basis.

[0042] In a specific implementation, the method of using a drone equipped with a high-definition camera to collect regular or real-time images of manhole covers on urban roads includes:

[0043] Hardware configuration: The drone is equipped with a high-definition camera and a GPS module.

[0044] Flight path planning: preset flight routes to ensure coverage of all manhole cover areas that need to be monitored.

[0045] Image acquisition frequency: Set the acquisition frequency according to actual needs (such as once an hour or once a day).

[0046] Data transmission: The collected images are transmitted to the ground control center in real time via wireless network.

[0047] In a specific implementation, the preset flight route ensures coverage of all manhole cover areas that need to be monitored. Multiple factors need to be considered, including flight safety, coverage range, image quality, battery life, data transmission efficiency, etc. The specific steps are as follows:

[0048] 1. Preparation

[0049] a. Collect data: Get a map of the city roads, including the location of all manhole covers. Collect weather data to ensure that flight conditions are suitable. Understand local drone flight regulations to ensure that the flight is legal.

[0050] b. Equipment preparation: Confirm that the drone and camera equipment are in good condition. Prepare backup batteries and storage devices. Confirm that the data transmission equipment (such as real-time transmission module) is working properly.

[0051] 2. Planning stage

[0052] a. Determine the flight area: Determine the flight area that needs to be covered based on the collected manhole cover location information. Divide the flight area into several smaller sub-areas for easier management and flight.

[0053] b. Plan the flight path: Use a path planning algorithm (such as A* algorithm, Dijkstra algorithm or RRT algorithm) to plan the flight path of the drone. Consider the flight altitude, speed and camera angle to ensure that the manhole cover image can be captured clearly. Consider avoiding obstacles such as trees, poles and buildings when planning the path.

[0054] c. Optimize the path: Optimize the flight path to reduce flight time and energy consumption. Consider the battery life of the drone to ensure that it can complete the entire flight mission. Plan the return point so that it can return safely when the battery is low.

[0055] 3. Execution phase

[0056] a. Pre-flight check: Perform a pre-flight check to ensure that all equipment is functioning properly. Check weather conditions to ensure that they meet the flight conditions.

[0057] b. Takeoff and flight: Start the drone flight according to the planned path. Monitor the drone's flight status and image acquisition in real time. Adjust the flight path and altitude according to actual conditions.

[0058] c. Data collection and transmission: Ensure that the high-definition camera works normally and collects clear images of the manhole cover. Transmit the collected image data to the ground control station in real time or regularly. Monitor the data transmission process to ensure that the data is complete and correct.

[0059] 4. Subsequent processing

[0060] a. Data processing and analysis: Post-process the collected image data, such as image correction, stitching, etc. Analyze the image data and identify the status and location of the manhole cover.

[0061] b. Report and feedback: Generate reports to record the status and location of manhole covers. According to the analysis results, make maintenance plans or carry out necessary repairs.

[0062] c. Maintenance and optimization: Optimize the path planning according to the execution situation. Regularly maintain the drone and camera equipment to ensure long-term stable operation.

[0063] Through the above steps, drones can be effectively used to collect regular or real-time images of manhole covers on urban roads, thereby improving the efficiency and accuracy of urban management.

[0064] Furthermore, the preprocessing of the collected images includes: denoising, contrast enhancement, image enhancement and image cropping. Specifically, the collected images are preprocessed to improve the accuracy of subsequent image recognition. Denoising: Use Gaussian filtering, median filtering and other methods to remove noise in the image. Image enhancement: Adjust contrast, brightness, etc. to make the manhole cover more obvious. Image cropping: Crop the area containing the manhole cover according to GPS data to reduce the amount of calculation.

[0065] Furthermore, the method also includes: after generating the alarm information according to the judgment result, sending the alarm information to the background through the wireless communication network.

[0066] Furthermore, it also includes a database, which stores the collected urban road rainwater and sewage manhole cover images and recognition results in the database for subsequent query and analysis.

[0067] Furthermore, the database is a MySQL or MongoDB database with a reasonably designed data table structure. The database is used to store image file paths, recognition results and timestamp information, and provides an API interface for querying historical data and analyzing results.

[0068] Furthermore, the image recognition model is constructed to recognize the preprocessed image, and the position status of the manhole cover is recognized, including:

[0069] Build an image recognition model based on the YOLOv5 model;

[0070] Collect annotated manhole cover image data, including normal manhole cover, missing manhole cover and displaced manhole cover image data, and construct annotated data sets;

[0071] Train the image recognition model based on the labeled dataset and optimize the model parameters;

[0072] The preprocessed image is input into the trained image recognition model to obtain the location and status of the manhole cover.

[0073] In a specific implementation, the position status of the manhole cover is identified using a convolutional neural network (CNN) as the basic architecture and combined with a target detection algorithm such as YOLO (You Only Look Once), Faster RCNN (Region based Convolutional Neural Networks) or SSD (Single Shot Multi Box Detector). YOLOv5 is used as an example here because it provides a good balance between speed and accuracy.

[0074] (1) The YOLOv5 model structure includes:

[0075] 1. Input layer: receives the preprocessed image.

[0076] 2. Backbone network: CSPDarknet53 is usually used as the backbone network for feature extraction.

[0077] 3. Feature Pyramid Network (FPN): used to fuse features at different scales.

[0078] 4. Detection head: contains multiple convolutional layers for predicting bounding boxes, class probabilities, and confidences.

[0079] 5. Output layer: Outputs the predicted bounding box, category, and confidence.

[0080] (2) Model training steps:

[0081] 1. Data preparation: Collect an image dataset containing manhole covers and annotate them (bounding boxes and categories). Divide the dataset into training, validation, and test sets.

[0082] 2. Data enhancement: Apply data enhancement techniques (such as rotation, scaling, cropping, color transformation, etc.) to increase data diversity.

[0083] 3. Model training: Use the pre-trained YOLOv5 model as a starting point. Set training parameters such as learning rate, batch size, optimizer, etc. Train the model on the training set and verify the performance on the validation set. Use early stopping and learning rate adjustment strategies to prevent overfitting.

[0084] 4. Model evaluation: Evaluate model performance on the test set, using indicators such as mAP (meanAveragePrecision), precision, recall, etc.

[0085] 5. Model optimization: Adjust the model structure and training parameters based on the evaluation results. Repeat the training and evaluation process until satisfactory performance is achieved.

[0086] (3) Detection steps:

[0087] 1. Image preprocessing: perform preprocessing operations such as scaling and normalization on the input image.

[0088] 2. Model inference: Input the preprocessed image into the trained YOLOv5 model. The model outputs the predicted bounding box, category, and confidence.

[0089] 3. Post-processing: Apply non-maximum suppression (NMS) to filter overlapping bounding boxes. Filter out the final detection results based on the confidence threshold.

[0090] 4. Result visualization: Draw the detected manhole cover bounding boxes and category labels on the original image.

[0091] 5. Status analysis: Analyze the status of the manhole cover (such as damage, missing, etc.) based on the detected manhole cover location and category.

[0092] 6. Report generation: Generate a report containing manhole cover location and status information.

[0093] Furthermore, the comparison and judgment of the position state of the identified manhole cover with the preset standard state includes: defining the preset standard state, entering the appearance shapes of various possible rainwater and sewage manhole covers on the road and their accurate positions on the road into the comparison database, so as to facilitate the subsequent image comparison to determine whether the manhole cover is lost or shifted. Define the appearance features of a normal manhole cover (such as shape, color, position, etc.), compare the recognition result with the preset standard state, and judge the abnormal type classification including: lost or shifted.

[0094] Furthermore, generating alarm information according to the judgment result includes: generating alarm information including the manhole cover location, status and timestamp, sending the alarm information via SMS or email, and recording detailed information of each alarm for subsequent query and analysis.

[0095] In a specific embodiment, a monitoring and alarm system for the loss or displacement of rainwater and sewage manhole covers on urban roads based on a drone image recognition system is provided. The drone is equipped with a high-definition camera and a GPS module, and regularly flies on a preset flight route to collect images of rainwater and sewage manhole covers on urban roads. The images are transmitted to the ground control center via a wireless network, where they are preprocessed and recognized. The recognition results are compared with the preset standard status. If the manhole cover is found to be lost or displaced, the system will automatically generate an alarm message and send it to the relevant management department and emergency response team via SMS or email.

[0096] The specific steps include:

[0097] Data collection: Use drones equipped with high-definition cameras to collect regular or real-time images of rainwater and sewage manhole covers on urban roads.

[0098] Image preprocessing: Preprocess the collected images, including denoising, contrast enhancement, etc., to improve the accuracy of subsequent image recognition.

[0099] Image recognition: Use deep learning algorithms to identify the pre-processed images and the location and status of manhole covers.

[0100] Status analysis: Compare the identified manhole cover status with the preset standard status to determine whether the manhole cover is missing or displaced.

[0101] Alarm processing: If the manhole cover is detected to be missing or displaced, the system will automatically generate an alarm message and send it to the relevant management department and emergency response team through the wireless communication network.

[0102] Data storage and backtracking: The collected images and recognition results are stored in the database for subsequent query and analysis.

[0103] The present invention provides an efficient and real-time urban road rain and sewage manhole cover monitoring and alarm system, which can effectively improve the safety and integrity of the manhole cover and ensure the safety of pedestrians and vehicles.

[0104] In a specific implementation, a city road rainwater and sewage manhole cover safety monitoring system, such as Figure 2 As shown, including:

[0105] Acquisition module: used to collect images of rainwater and sewage manhole covers on urban roads;

[0106] Preprocessing module: used to preprocess the collected images;

[0107] Recognition module: used to build an image recognition model to recognize the preprocessed image and identify the position status of the manhole cover;

[0108] Comparison module: used to compare the position status of the identified manhole cover with the preset standard status;

[0109] Alarm module: used to generate alarm information based on the judgment results.

[0110] The present invention proposes a method for monitoring the safety of urban road rainwater and sewage manhole covers, which realizes real-time monitoring: drones can quickly cover large areas and realize real-time monitoring of manhole covers. Efficient identification: deep learning algorithms can accurately identify the location and status of manhole covers and improve monitoring efficiency. Automatic alarm: once an abnormal situation is detected, the system can automatically alarm, reduce human intervention, and improve response speed. Data accumulation: all data are stored for subsequent analysis and decision support.

[0111] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0112] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring the safety of urban road rainwater and sewage manhole covers, characterized in that: include: Collect images of rainwater and sewage manhole covers on urban roads; Preprocess the collected images; Construct an image recognition model to recognize the preprocessed image and identify the position status of the manhole cover; Compare and judge the position status of the identified manhole cover with the preset standard status; Generate alarm information based on the judgment result.

2. A method for monitoring the safety of urban road rainwater and sewage manhole covers according to claim 1, characterized in that: The collecting of images of rainwater and sewage manhole covers on urban roads includes: using a drone equipped with a high-definition camera to collect images of rainwater and sewage manhole covers on urban roads regularly or in real time.

3. A method for monitoring the safety of urban road rainwater and sewage manhole covers according to claim 1, characterized in that: The preprocessing of the collected images includes: denoising, contrast enhancement, image enhancement and image cropping.

4. A method for monitoring the safety of urban road rainwater and sewage manhole covers according to claim 1, characterized in that: Also includes: After the alarm information is generated according to the judgment result, the alarm information is sent to the background through the wireless communication network.

5. A method for safety monitoring of urban road rainwater and sewage manhole covers according to claim 1, characterized in that: It also includes a database, which stores the collected urban road rainwater and sewage manhole cover images and recognition results in the database for subsequent query and analysis.

6. A method for safety monitoring of urban road rainwater and sewage manhole covers according to claim 5, characterized in that: The database is a MySQL or MongoDB database, which is used to store image file paths, recognition results and timestamp information, and provides an API interface for querying historical data and analyzing results.

7. A method for monitoring the safety of urban road rainwater and sewage manhole covers according to claim 1, characterized in that: The image recognition model is constructed to recognize the preprocessed image, and the position status of the manhole cover is recognized, including: Build an image recognition model based on the YOLOv5 model; Collect annotated manhole cover image data, including normal manhole cover, missing manhole cover and displaced manhole cover image data, and construct annotated data sets; Train the image recognition model based on the labeled dataset and optimize the model parameters; The preprocessed image is input into the trained image recognition model to obtain the location and status of the manhole cover.

8. A method for safety monitoring of urban road rainwater and sewage manhole covers according to claim 1, characterized in that: The comparing and judging the position state of the identified manhole cover with the preset standard state includes: defining the preset standard state, defining the appearance characteristics of a normal manhole cover, comparing the identification result with the preset standard state, and judging the abnormal type classification including: loss or displacement.

9. A method for safety monitoring of urban road rainwater and sewage manhole covers according to claim 1, characterized in that: The generating of alarm information according to the judgment result includes: generating alarm information including the location, status and timestamp of the manhole cover, sending the alarm information via SMS or email, and recording detailed information of each alarm.

10. A safety monitoring system for urban road rainwater and sewage manhole covers, characterized in that: include: Acquisition module: used to collect images of rainwater and sewage manhole covers on urban roads; Preprocessing module: used to preprocess the collected images; Recognition module: used to build an image recognition model to recognize the preprocessed image and identify the position status of the manhole cover; Comparison module: used to compare the position status of the identified manhole cover with the preset standard status; Alarm module: used to generate alarm information based on the judgment results.

Images