Using method of intelligent zebra crossing early warning system for visual blind area of construction road

Abstract

The invention discloses a use method of an intelligent zebra crossing early warning system for a visual blind area of a construction road. The use method is a process of deploying and using an early warning system comprising a high-definition camera, an AI edge calculation box, an integrated controller and an IP network loudspeaker. Firstly, a high-definition camera is installed to fully cover a blind area range, an AI edge computing box, an integrated controller and an IP network loudspeaker are installed at proper positions of an office area, and the integrated controller is in communication connection with the AI edge computing box and the IP network loudspeaker; establishing data transmission connection between the AI edge calculation box and the high-definition camera; the AI edge calculation box carries out training and optimization on collected engineering vehicle materials, when an engineering vehicle enters a designated area, a trigger signal is generated immediately and transmitted to the integrated controller, and the IP network loudspeaker is controlled to start and close a voice program. By applying the technology, people and vehicle conditions in the visual blind area of the construction road can be informed in real time, and collision accidents caused by the visual blind area or delayed response of a driver are avoided.

Description

Technical Field

[0001] This invention relates to the technical field of intelligent transportation, specifically a method for using an intelligent zebra crossing early warning system for blind spots on construction access roads. Background Technology

[0002] During construction, access roads serve as vital passageways for personnel and materials, making their safety paramount. However, due to the complex terrain of these access roads, numerous blind spots often exist at office area entrances, such as sharp turns, steep slopes, and areas obscured by obstacles. Within these blind spots, pedestrians and construction vehicles (such as forklifts and dump trucks) cannot detect each other in time, greatly increasing the risk of collisions.

[0003] Traditionally, static zebra crossings are installed at the entrances and exits of office areas to guide pedestrian traffic. However, this method has many drawbacks. On the one hand, it fails to provide warnings in blind spots and lacks dynamic perception and proactive intervention capabilities. On the other hand, construction environments are complex and changeable, and traditional zebra crossings suffer from poor adaptability, inability to prevent dangerous situations in real time, and low traffic efficiency (such as pedestrians "testing the waters" causing vehicles to repeatedly start and stop). Therefore, there is a need to design an intelligent zebra crossing early warning technology for blind spots in construction access roads. Summary of the Invention

[0004] The purpose of this invention is to address the problems existing in the prior art by providing a method for using an intelligent zebra crossing early warning system for blind spots in construction access roads. This technology integrates an intelligent early warning algorithm into the blind spots of construction access roads. Based on video recognition technology, it can monitor the status of people and vehicles in the blind spots in real time, proactively prevent pedestrian-vehicle conflicts, promptly block dangerous scenarios, and avoid collisions caused by blind spots or delayed driver reactions. It possesses intelligent perception and early warning capabilities. The system is highly adaptable and can meet the needs of different construction scenarios. It features low-cost upgrades, easy turnover, and no pollution. By optimizing traffic efficiency and preventing pedestrians from "trial crossings" that cause vehicles to repeatedly start and stop, it greatly improves the traffic safety level and traffic flow in construction access road areas.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A smart zebra crossing early warning system for blind spots in construction access roads includes a video data acquisition module, a data analysis module, and an on-site execution module. The video data acquisition module includes one or more high-definition cameras for monitoring the entrance, sharp turns, and steep slopes of the blind spots in the construction access road. These high-definition cameras can be bolted to the side wall of the office area. The data analysis module includes an AI edge computing box, which deploys AI algorithms and intelligent early warning algorithms. The AI ​​algorithms include algorithms for recognizing the front and rear of construction vehicles and for recognizing the up and down directions of vehicles entering or leaving the electronic fence. The on-site execution module includes an integrated controller and an IP network speaker. The integrated controller has a built-in backup power supply. The integrated controller and the AI ​​edge computing box are installed in the office area. The integrated controller can be bolted to the side wall of the office area, and the IP network speaker is fixed to the top of the office entrance. The AI ​​edge computing box and the high-definition cameras are connected via signal matching to complete video material acquisition and AI algorithm training. A wired connection is used between the AI ​​edge computing box and the integrated controller to generate trigger signals and transmit them to the integrated controller. The integrated controller is equipped with a wireless transmission module that connects to the IP network speaker, controlling the IP network speaker to activate a voice program for voice warnings.

[0006] A further preferred embodiment: the on-site execution module also includes a projector. The integrated controller is connected to the projector via a wired connection. The projector is installed in a suitable location in the office area, and projects dynamic zebra crossings onto the pedestrian walkway area. The integrated controller controls the projector after receiving a signal from the AI ​​edge computing box.

[0007] A further preferred embodiment: the field execution module also includes a multi-frequency flashlight, which is bolted to the top of the office area entrance. The integrated controller controls the multi-frequency flashlight after receiving a signal from the AI ​​edge computing box.

[0008] The steps for using the above-mentioned early warning system are as follows: A. System Deployment: Based on the construction site conditions, video data acquisition modules will be rationally installed at key locations such as entrances to blind spots, sharp turns, and steep slopes along the construction access road, taking into account the actual terrain and field of vision, to ensure comprehensive coverage of the blind spots. Projector modules will be installed in suitable locations in the office area to ensure that the projected dynamic zebra crossings are clearly displayed on the key passage areas of the construction access road. Audible and visual alarm modules will be evenly distributed around the construction access road to ensure that warning signals effectively cover the entire blind spot. The integrated controller will be installed in a location that is easy to manage and maintain, and a reliable communication connection will be established with the AI ​​edge computing box, projector, IP network speaker, and multi-frequency flashlight to enable collaborative work between the modules. The AI ​​edge computing box will be installed in a safe location close to the video data acquisition modules, and a stable data transmission connection will be established with the video data acquisition modules wirelessly.

[0009] B. Video footage collection, AI algorithm training and core logic: Collect video surveillance footage of engineering vehicles passing through sharp turns and steep slopes in front of and behind the office area, convert it into image footage, label the front and rear of the engineering vehicles, use the labeled image footage to train and optimize the AI ​​algorithm, then deploy the trained AI algorithm to the AI ​​edge computing box, and set up electronic fences at a safe distance from the sharp turns and steep slopes in front of and behind the office area to the section of road directly in front of the office area based on the field of view of each camera.

[0010] The core logic of AI algorithms can be divided into three main stages: target recognition and analysis, event verification and confirmation, and alarm signal response.

[0011] Phase 1: Target recognition and analysis, including image standardization processing, core AI engine analysis, and preliminary result refinement. Preliminary structure refinement includes confidence threshold filtering and redundant detection merging.

[0012] Image standardization processing: The raw video stream captured by the high-definition camera is analyzed frame by frame and standardized. Without distorting the image content and maintaining the original aspect ratio, the image is uniformly scaled and adjusted to the standard size W×H required by the AI ​​analysis engine by center cropping.

[0013] Core AI Engine Analysis: Standardized images are fed into a deep learning neural network model (AI engine) for core analysis, enabling rapid and accurate scanning, positioning, and identification of preset engineering vehicle targets in images.

[0014] Preliminary results refined: Confidence threshold filtering: When the confidence level assigned to each identified engineering vehicle target by the system is higher than the preset recognition confidence threshold, the target is considered to be effectively identified.

[0015] Redundant detection merging: When the AI ​​engine generates multiple highly overlapping detection boxes for the same engineering vehicle target, the system will automatically start and optimize the program to analyze these overlapping detection results. When the overlap of multiple detection boxes is greater than the set non-maximum suppression threshold, the unique detection box with the highest confidence is retained as the final recognition result.

[0016] Phase Two: Event Verification and Confirmation, including spatial rule verification, temporal persistence verification, event recording and cooling-off.

[0017] Spatial rule verification (area filtering): Electronic fences are pre-set in the monitoring images of the upper and lower cameras respectively. When the engineering vehicle touches the electronic fence set by the upper camera and enters the key monitoring area, the subsequent time-based continuous verification logic is triggered.

[0018] Time continuity verification (anti-shake confirmation): When the same engineering vehicle is continuously and uninterruptedly observed in the key monitoring area for a specified number of video frames, which is greater than the set number of consecutive frame confirmations N, the system determines it as a real and valid event.

[0019] Event Logging and Cooling-Off: Once an action passes all the above verifications, the system will officially log it as an alarm event (stored in the database, generating an annotated snapshot, etc.). Simultaneously, to avoid repeatedly logging the same persistent event, the system will implement an alarm logging cooldown period for that event type. During the cooldown period, even if the action continues, the system will not generate new alarm records, ensuring the simplicity and efficiency of the event log.

[0020] Phase 3: Alarm signal response, including continuous status confirmation and alarm silence period. Continuous Status Confirmation: Before outputting a signal, the system performs another high-level continuous assessment. If the percentage of engineering vehicle frames detected within the continuous confirmation time window exceeds the set alarm trigger ratio, the system determines it as a stable and continuous event and outputs an alarm signal.

[0021] Alarm Silence Period: After a complete round of signal output, in order to prevent "alarm fatigue" and provide a window of opportunity for on-site handling, the alarm algorithm enters a silence period (or "refractory period"). During this period, even if the AI ​​algorithm still detects events that meet the criteria, it will not output a signal until the silence period ends.

[0022] C. Intelligent Early Warning Algorithm: Intelligent Early Warning Algorithm for Vehicles Preparing to Enter the Construction Access Road in Front of the Office Area: When the AI ​​edge computing box detects, through its trained AI algorithm, that the front of a construction vehicle is about to touch the electronic fence set by the camera above, it immediately generates a trigger signal and transmits it to the integrated controller. Upon receiving the signal, the integrated controller controls the IP network speaker to start a voice prompt, playing three times at a set volume (85dB) and interval (2s) stating that a construction vehicle is approaching from above and passage is prohibited. When the algorithm detects that the front of a construction vehicle below touches the electronic fence preset by the camera below, it immediately generates a trigger signal and transmits it to the integrated controller. After receiving the signal, the integrated controller controls the IP network speaker to start a voice prompt, playing three times in a loop at a set volume (85db) and interval (2s) that a construction vehicle is approaching from below and passage is prohibited; it controls the projector to execute a display content switching program, changing the ground projection pattern from a zebra crossing style to a no-pass sign; and it controls the multi-frequency flashlight to start a high-frequency flashing mode, using alternating red and blue strong light to warn passing personnel. Intelligent early warning algorithm for construction access road section ahead of the office area: When the AI ​​edge computing box identifies, through the trained AI algorithm, that the front of an overhead construction vehicle touches the electronic fence preset by the camera below, or vice versa, it immediately generates a trigger signal and transmits it to the integrated controller. Upon receiving the signal, the integrated controller controls the IP network speaker to mute the voice program; controls the projector to execute the display content switching program, changing the ground projection pattern from a no-entry sign to a zebra crossing pattern; and controls the activation of the multi-frequency flashlight to deactivate the high-frequency flashing mode. Intelligent early warning algorithm for power outage or communication interruption: When power outage or communication interruption occurs, the integrated controller controls the IP network speaker to start a voice program to prompt the device malfunction in a loop at a set volume (85dB) and interval (2s); controls the projector to execute a display content switching program, switching the ground projection pattern from a zebra crossing style to a no-entry sign; controls the activation of the multi-frequency flashlight to start a high-frequency flashing mode, using alternating red and blue strong light to warn passing personnel; Intelligent early warning algorithm for power or communication restoration: When power or communication is restored, the integrated controller controls the IP network speaker to shut down the voice program; controls the projector to execute the display content switching program, switching the ground projection pattern from a no-entry sign to a zebra crossing pattern; and controls the activation of the multi-frequency flashlight to disable the high-frequency flashing mode.

[0023] This technology applies intelligent early warning algorithms to the blind spots of construction access roads, monitoring the dynamics of people and vehicles in these blind spots in real time. Its highly adaptable design caters to various complex construction scenarios. After system data processing, it provides intelligent alerts. When a vehicle enters the section of road ahead of the office area and touches the designated electronic fence, the IP network speaker activates a voice program, repeatedly playing a voice prompt that construction vehicles are approaching and passage is prohibited, demonstrating intelligent perception and early warning capabilities. The projector executes a content switching program, changing the ground projection pattern from a zebra crossing style to a "No Entry" sign. It also controls the activation of multi-frequency flashlights in a high-frequency flashing mode, using alternating red and blue strong light to warn passersby. Compared to traditional zebra crossings, this technology allows for low-cost upgrades, is reusable, and is environmentally friendly and pollution-free. It also reduces labor costs, saves time and effort, and is safer. Attached Figure Description

[0024] Figure 1 This is the logic diagram of the intelligent zebra crossing early warning system for blind spots on construction access roads; Figure 2 This is a schematic diagram of the three-dimensional layout structure of the intelligent zebra crossing early warning system for blind spots on construction access roads. Figure 3 This is a top-down view of the layout structure of the intelligent zebra crossing early warning system for blind spots on construction access roads; The names corresponding to the serial numbers in the figure are: 1. Construction access road; 2. Office area; 3. High-definition camera; 4. Projector; 5. IP network speaker; 6. Multi-frequency flashlight; 7. Integrated control module; 8. AI edge computing box. Detailed Implementation

[0025] To provide a more detailed description of the present invention, the following description is provided in conjunction with embodiments and accompanying drawings. Example

[0026] A smart zebra crossing early warning system for blind spots in construction access roads is disclosed. The system comprises three main modules: a video data acquisition module, a data analysis module, and an on-site execution module. The video data acquisition module includes one or more high-definition cameras 3. The data analysis module includes an AI edge computing box 8. The on-site execution module includes an integrated controller 7, a projector 4, an IP network speaker 5, and a multi-frequency flashlight 6. The components are matched and connected to form a smart zebra crossing early warning system for blind spots in construction access roads.

[0027] The high-definition camera 3 is bolted to the side wall of the office area 2; the AI ​​edge computing box 8 is installed inside the office area 2; the integrated controller 7 is bolted to the side wall of the office area 2; the integrated controller 7 is connected to the projector 4 and the multi-frequency flash 6 via a wired connection; the integrated controller has a wireless transmission module and is connected to the IP network speaker 5 via a signal; the projector 4 is bolted to the top of the office area 2; the IP network speaker 5 is bolted to the top of the office area entrance; the multi-frequency flash 6 is bolted to the top of the office area entrance.

[0028] The AI ​​edge computing box is equipped with AI recognition algorithms and intelligent early warning algorithms. The AI ​​recognition algorithms include algorithms for recognizing the front and rear of engineering vehicles, and algorithms for recognizing the up and down directions of vehicles entering or leaving the electronic fence. The intelligent early warning algorithms include algorithms for early warning of construction access road sections ahead of vehicles preparing to enter the office area, and algorithms for early warning of construction access road sections ahead of vehicles leaving the office area.

[0029] The steps for using the above-mentioned early warning system are as follows: A. System Deployment: Based on the construction site conditions, video data acquisition modules will be rationally installed at key locations such as the entrance to the blind spot of construction access road 1, sharp turns, and steep slopes, according to the actual terrain and field of vision, to ensure comprehensive coverage of the blind spot area; projector modules will be installed in suitable locations in the office area to ensure that the projected dynamic zebra crossings are clearly displayed on the key passage areas of the construction access road; sound and light alarm modules will be evenly distributed around the construction access road to ensure that the warning signals can effectively cover the entire blind spot; the integrated controller will be installed in a location that is easy to manage and maintain, and a reliable communication connection will be established with the AI ​​edge computing box, projector, IP network speaker, and multi-frequency flashlight to achieve collaborative work between the modules; the AI ​​edge computing box will be installed in a safe location close to the video data acquisition modules, and a stable data transmission connection will be established with the video data acquisition modules wirelessly.

[0030] B. Video footage collection, AI algorithm training and core logic: Collect video surveillance footage of engineering vehicles passing through sharp turns and steep slopes in front of and behind the office area, convert it into image footage, label the front and rear of the engineering vehicles, use the labeled image footage to train and optimize the AI ​​algorithm, then deploy the trained AI algorithm to the AI ​​edge computing box, and set up electronic fences at a safe distance from the sharp turns and steep slopes in front of and behind the office area to the section of road directly in front of the office area based on the field of view of each camera.

[0031] The core logic of AI algorithms can be divided into three main stages: target recognition and analysis, event verification and confirmation, and alarm signal response.

[0032] Phase 1: Target recognition and analysis, including image standardization processing, core AI engine analysis, and preliminary result refinement. Preliminary structure refinement includes confidence threshold filtering and redundant detection merging.

[0033] Image standardization processing: The original video stream captured by the high-definition camera is analyzed frame by frame and standardized. Without distorting the image content and maintaining the original aspect ratio, the image is uniformly scaled and adjusted to the standard size W×H (640×640px) required by the AI ​​analysis engine by center cropping.

[0034] Core AI Engine Analysis: Standardized images are fed into a deep learning neural network model (AI engine) for core analysis, enabling rapid and accurate scanning, positioning, and identification of preset engineering vehicle targets in images.

[0035] Preliminary results refined: Confidence threshold filtering: When the confidence level assigned by the system to each identified engineering vehicle target is higher than the preset recognition confidence threshold (0.6), the target is considered to be effectively identified.

[0036] Redundant detection merging: When the AI ​​engine generates multiple highly overlapping detection boxes for the same engineering vehicle target, the system will automatically start and optimize the program to analyze these overlapping detection results. When the overlap of multiple detection boxes is greater than the set non-maximum suppression threshold (0.45), the single detection box with the highest confidence is retained as the final recognition result.

[0037] Phase Two: Event Verification and Confirmation, including spatial rule verification, temporal persistence verification, event recording and cooling-off.

[0038] Spatial rule verification (area filtering): Electronic fences are pre-set in the monitoring images of the upper and lower cameras respectively. When the engineering vehicle touches the electronic fence set by the upper camera and enters the key monitoring area, the subsequent time-based continuous verification logic is triggered.

[0039] Time continuity verification (anti-shake confirmation): When the same engineering vehicle is continuously and uninterruptedly observed in the key monitoring area for a specified number of video frames, which is greater than the set number of consecutive frame confirmations N (15), the system determines it as a real and valid event.

[0040] Event Logging and Cooling-Off: Once an action passes all the above verifications, the system will officially log it as an alarm event (stored in the database, generating a tagged snapshot, etc.). Simultaneously, to avoid repeatedly logging the same persistent event, the system will implement an alarm logging cooldown period (15 seconds) for that event type. During the cooldown period, even if the action continues, the system will not generate new alarm records, ensuring the simplicity and efficiency of the event log.

[0041] Phase 3: Alarm signal response, including continuous status confirmation and alarm silence period. Continuous Status Confirmation: Before signal output, the system performs another high-level continuous assessment. If the percentage of engineering vehicle frames detected within the continuous confirmation time window (3s) exceeds the set alarm trigger ratio (0.5), the system determines it as a stable and continuous event and outputs an alarm signal.

[0042] Alarm Silence Period: After a complete round of signal output, in order to prevent "alarm fatigue" and provide a window of opportunity for on-site handling, the alarm algorithm enters a silence period (or "refractory period"). During this period, even if the AI ​​algorithm still detects events that meet the criteria, it will not output a signal until the silence period ends.

[0043] C. Intelligent Early Warning Algorithm: Intelligent Early Warning Algorithm for Vehicles Preparing to Enter the Construction Access Road in Front of the Office Area: When the AI ​​edge computing box detects, through its trained AI algorithm, that the front of a construction vehicle is about to touch the electronic fence set by the camera above, it immediately generates a trigger signal and transmits it to the integrated controller. Upon receiving the signal, the integrated controller controls the IP network speaker to start a voice prompt, playing three times at a set volume (85dB) and interval (2s) stating that a construction vehicle is approaching from above and passage is prohibited. When the algorithm detects that the front of a construction vehicle below touches the electronic fence preset by the camera below, it immediately generates a trigger signal and transmits it to the integrated controller. After receiving the signal, the integrated controller controls the IP network speaker to start a voice prompt, playing three times in a loop at a set volume (85db) and interval (2s) that a construction vehicle is approaching from below and passage is prohibited; it controls the projector to execute a display content switching program, changing the ground projection pattern from a zebra crossing style to a no-pass sign; and it controls the multi-frequency flashlight to start a high-frequency flashing mode, using alternating red and blue strong light to warn passing personnel. Intelligent early warning algorithm for construction access road section ahead of the office area: When the AI ​​edge computing box identifies, through the trained AI algorithm, that the front of an overhead construction vehicle touches the electronic fence preset by the camera below, or vice versa, it immediately generates a trigger signal and transmits it to the integrated controller. Upon receiving the signal, the integrated controller controls the IP network speaker to mute the voice program; controls the projector to execute the display content switching program, changing the ground projection pattern from a no-entry sign to a zebra crossing pattern; and controls the activation of the multi-frequency flashlight to deactivate the high-frequency flashing mode. Intelligent early warning algorithm for power outage or communication interruption: When power outage or communication interruption occurs, the integrated controller controls the IP network speaker to start a voice program to prompt the device malfunction in a loop at a set volume (85dB) and interval (2s); controls the projector to execute a display content switching program, switching the ground projection pattern from a zebra crossing style to a no-entry sign; controls the activation of the multi-frequency flashlight to start a high-frequency flashing mode, using alternating red and blue strong light to warn passing personnel; Intelligent early warning algorithm for power or communication restoration: When power or communication is restored, the integrated controller controls the IP network speaker to shut down the voice program; controls the projector to execute the display content switching program, switching the ground projection pattern from a no-entry sign to a zebra crossing pattern; and controls the activation of the multi-frequency flashlight to disable the high-frequency flashing mode.

[0044] Implementation Case: The construction access road at a certain construction site is 6 meters wide and features a two-lane, two-way design. The office area is located on one side of the access road. Above the office area is a sharp bend in the access road, and below is a steep slope, creating blind spots when entering or exiting the office area. A smart zebra crossing warning system for blind spots in the construction access road has been installed. Its operation steps are as follows: 1. System Deployment: Based on the terrain of the construction site and the visibility of the office area, high-definition cameras are installed on the upper and lower sides of the office area, facing the sharp turns and steep slopes of the construction access road above and below the office area; a projector is installed at the top center of the office area to ensure that the projected dynamic zebra crossings and no-entry signs are clearly displayed on the construction access road in front of the office area; IP network speakers and multi-frequency flashlights are evenly distributed at the entrance of the office area and around the construction access road to ensure that the warning signals can effectively cover the entire blind spot; an integrated controller is installed at the entrance of the office area for easy management and maintenance, and is connected to the AI ​​edge computing box, projector, and multi-frequency flashlights via wired connection, and to the IP network speakers via wireless signal to achieve collaborative work between the modules; the AI ​​edge computing box is installed inside the office area and establishes a stable data transmission connection with the video data acquisition module wirelessly.

[0045] 2. Video footage collection, AI algorithm training, and core logic: Collect video surveillance footage of construction vehicles such as concrete mixer trucks, excavators, and dump trucks passing through sharp turns and steep slopes in front of and behind the office area. After converting the footage into image footage, label the front and rear of the vehicles. Use the labeled image footage to train and optimize the AI ​​algorithm. Then, deploy the trained AI algorithm to an AI edge computing box and set up electronic fences at a distance of 10 meters from the sharp turns and steep slopes in front of and behind the office area.

[0046] 3. Intelligent early warning algorithm: Intelligent early warning algorithm for the construction access road section in front of the office area: When the AI ​​edge computing box detects, through its trained AI algorithm, that the front of a construction vehicle is touching the electronic fence preset by the camera above, it immediately generates a trigger signal and transmits it to the integrated controller. Upon receiving the signal, the integrated controller controls the IP network speaker to activate a voice prompt, playing three times at a set volume (85dB) and interval (2s) stating that a construction vehicle is approaching from above and passage is prohibited. When the algorithm detects that the front of a construction vehicle below touches the electronic fence preset by the camera below, it immediately generates a trigger signal and transmits it to the integrated controller. After receiving the signal, the integrated controller controls the IP network speaker to start a voice prompt, playing three times in a loop at a set volume (85db) and interval (2s) that a construction vehicle is approaching from below and passage is prohibited; it controls the projector to execute a display content switching program, changing the ground projection pattern from a zebra crossing style to a no-pass sign; and it controls the multi-frequency flashlight to start a high-frequency flashing mode, using alternating red and blue strong light to warn passing personnel. Intelligent early warning algorithm for construction access road section ahead of the office area: When the AI ​​edge computing box identifies, through the trained AI algorithm, that the front of an overhead construction vehicle touches the electronic fence preset by the camera below, or vice versa, it immediately generates a trigger signal and transmits it to the integrated controller. Upon receiving the signal, the integrated controller controls the IP network speaker to mute the voice program; controls the projector to execute the display content switching program, changing the ground projection pattern from a no-entry sign to a zebra crossing pattern; and controls the activation of the multi-frequency flashlight to deactivate the high-frequency flashing mode. Intelligent early warning algorithm for power outage or communication interruption: When power outage or communication interruption occurs, the integrated controller controls the IP network speaker to start a voice program to prompt the device malfunction in a loop at a set volume (85db) and interval (2s); controls the projector to execute a display content switching program, switching the ground projection pattern from a zebra crossing style to a no-entry sign; controls the multi-frequency flashlight to start a high-frequency flashing mode, using alternating red and blue strong light to warn passing personnel.

[0047] Intelligent early warning algorithm for power or communication restoration: When power or communication is restored, the integrated controller controls the IP network speaker to shut down the voice program; controls the projector to execute the display content switching program, switching the ground projection pattern from a no-entry sign to a zebra crossing pattern; and controls the activation of the multi-frequency flashlight to disable the high-frequency flashing mode.

[0048] The above description is not intended to limit the present invention, nor is the present invention limited to the above examples. Any changes, modifications, additions or substitutions made by those skilled in the art within the scope of the present invention should be protected by the present invention.

Claims

1. A method for using an intelligent zebra crossing early warning system for blind spots in construction access roads, characterized in that: The early warning system includes a high-definition camera (3), an AI edge computing box (8), an integrated controller (7), and an IP network speaker (5); the usage method includes the following steps: A. System Deployment: Based on the construction site conditions, install high-definition cameras (3) to ensure full coverage of blind spots; install AI edge computing boxes (8), integrated controllers (7) and IP network speakers (5) in suitable locations in the office area, and establish reliable communication connections between the integrated controllers (7), AI edge computing boxes (8), and IP network speakers (5); establish stable data transmission connections between the AI ​​edge computing boxes (8) and high-definition cameras (3); B. Video footage collection, AI algorithm training and core logic: Collect video surveillance footage of engineering vehicles passing through sharp turns and steep slopes in front of and behind the office area, convert it into image footage, label the front and rear of the engineering vehicles, use the labeled image footage to train and optimize the AI ​​algorithm, then deploy the trained AI algorithm to the AI ​​edge computing box, and set up electronic fences at a safe distance from the sharp turns and steep slopes in front of and behind the office area to the section of road directly in front of the office area based on the field of view of each camera; C. Intelligent early warning algorithm: When the AI ​​edge computing box (8) identifies the front of the construction vehicle entering the touch electronic fence through the trained AI algorithm, it immediately generates a trigger signal and transmits it to the integrated controller (7). After receiving the signal, the integrated controller (7) controls the IP network speaker (5) to start the voice program and play the voice prompt that a construction vehicle is coming to the construction access road and that passage is prohibited. When the front of the construction vehicle leaves the touch electronic fence, the IP network speaker (5) controls the voice program to turn off.

2. The method of using the intelligent zebra crossing early warning system for blind spots in construction access roads according to claim 1, characterized in that: The warning system also includes a projector (4). The integrated controller (7) is connected to the projector (4) via a wired connection. The projector (4) is installed in a suitable location in the office area (2). The projector (4) projects a dynamic zebra crossing onto the construction access road. When the AI ​​edge computing box (8) identifies the front of the construction vehicle entering the electronic fence through the trained AI algorithm, it immediately generates a trigger signal and transmits it to the integrated controller (7). After receiving the signal, the integrated controller (7) controls the projector (4) to execute the display content switching program, changing the ground projection pattern from a zebra crossing pattern to a no-pass sign. When the front of the construction vehicle leaves the electronic fence, it immediately generates a trigger signal and transmits it to the integrated controller (7). After receiving the signal, the integrated controller (7) controls the projector (4) to execute the display content switching program, changing the ground projection pattern from a no-pass sign to a zebra crossing pattern.

3. The method of using the intelligent zebra crossing early warning system for blind spots in construction access roads according to claim 1 or 2, characterized in that: The warning system also includes a multi-frequency flashlight (6), which is fixed to the top of the office area (2) entrance by bolts. When the AI ​​edge computing box (8) identifies the front of the engineering vehicle entering the electronic fence through the trained AI algorithm, it immediately generates a trigger signal and transmits it to the integrated controller (7). After receiving the signal, the integrated controller (7) controls the multi-frequency flashlight (6) to start the high-frequency flashing mode and warns people passing by with alternating red and blue strong light. When the front of the engineering vehicle leaves the electronic fence, it immediately generates a trigger signal and transmits it to the integrated controller (7). After receiving the signal, the integrated controller (7) controls the multi-frequency flashlight (6) to turn off the high-frequency flashing mode.

4. The method of using the intelligent zebra crossing early warning system for blind spots in construction access roads according to claim 1, characterized in that: The core logic of the AI ​​algorithm is divided into three main stages: target recognition and analysis, event verification and confirmation, and alarm signal response.

5. The method of using the intelligent zebra crossing early warning system for blind spots in construction access roads according to claim 4, characterized in that: The target recognition and analysis includes image standardization, core AI engine analysis, and preliminary result refinement. Preliminary structural refinement includes confidence threshold filtering and redundancy detection merging. Image standardization involves parsing and standardizing the raw video stream captured by the high-definition camera frame by frame. Without distorting the image content and maintaining the original aspect ratio, the image is uniformly scaled and adjusted to the standard size W×H (640×640) required by the AI ​​analysis engine through center cropping. Core AI engine analysis involves feeding the standardized image into a deep learning neural network model (AI engine) for core analysis. The system enables rapid and accurate scanning, positioning, and identification of preset engineering vehicle targets in images. Confidence threshold filtering ensures that each identified engineering vehicle target is considered validly identified when the confidence level assigned by the system is higher than the preset recognition confidence threshold (0.6). Redundant detection merging automatically starts and optimizes the analysis of these overlapping detection results when the AI ​​engine generates multiple highly overlapping detection boxes for the same engineering vehicle target. When the overlap of multiple detection boxes is greater than the set non-maximum suppression threshold (0.45), the single detection box with the highest confidence level is retained as the final identification result.

6. The method of using the intelligent zebra crossing early warning system for blind spots in construction access roads according to claim 4, characterized in that: The event verification and confirmation include spatial rule verification, time-duration verification, event recording and cooling; spatial rule verification involves pre-setting electronic fences in the monitoring images of the upper and lower cameras respectively. When an engineering vehicle touches the electronic fence set by the upper camera and enters the key monitoring area, the subsequent time-duration verification logic is triggered. The time continuity verification is when the same engineering vehicle is continuously and uninterruptedly observed in the key monitoring area for a specified number of video frames, which is greater than the set number of consecutive frame confirmations N (15), the system determines it as a real and valid event; the event recording and cooling is that once an action passes all the above verifications, the system will officially record it as an alarm event.

7. The method of using the intelligent zebra crossing early warning system for blind spots in construction access roads according to claim 4, characterized in that: The alarm signal response includes continuous status confirmation and an alarm silence period. Continuous status confirmation involves the system performing a high-level continuous assessment before signal output. If the proportion of engineering vehicle frames detected within the continuous confirmation time window (3s) exceeds the set alarm trigger ratio (0.5), the system determines it as a stable and continuous event and outputs an alarm signal. The alarm silence period is a period after a complete round of signal output. To prevent "alarm fatigue" and provide a window for on-site handling, the alarm algorithm enters a silence period (or "refractory period"). During this period, even if the AI ​​algorithm still detects events that meet the conditions, it will not output a signal until the silence period ends.

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