KR20240015556A - Device for safety aid using a image - Google Patents

Abstract

The present invention relates to a safety assistance device using images that can be applied to transportation devices such as heavy equipment. In another embodiment, the safety assistance device using images is configured to be attachable and detachable from a transportation device and displays images in at least one direction. At least one video recording device that shoots and transmits, analyzing the patterns and direction of surrounding objects and the positions of objects in the captured video from the video data received from the at least one video recording device, and analyzing video data and map data according to the analysis results It includes an image processing device that generates and a monitoring module that displays analyzed image data and map data from the image processing device as images and sounds.

Description

Safety assistance device using images {DEVICE FOR SAFETY AID USING A IMAGE}

The present invention relates to a safety assistance device using images that can be applied to transportation devices such as heavy equipment.

Various industrial companies, such as construction sites and logistics warehouses, use heavy cargo transport devices such as forklifts and crane transformers to move heavy cargo.

When moving heavy goods using heavy goods transport equipment, there may be other workers or obstacles around the moving distance, so there is a high risk of safety accidents. In particular, when transportation work is performed in a narrow work space or a space with many blind spots for the driver's vision, there is a very high possibility that a collision with other surrounding objects or obstacles may occur due to the driver's error in position judgment or operation. Accordingly, there were many cases where an auxiliary worker was needed to guide the movement of the heavy goods transport equipment behind the heavy goods transport equipment or in blind spots.

In recent years, front and rear cameras have been installed on heavy equipment and transportation equipment to capture images in the corresponding direction and display surrounding images through display devices such as navigation, so that drivers can easily recognize the surrounding environment during transportation work. technology is supported.

Furthermore, in recent years, cameras have been installed in the front, rear, left, and right sides of heavy equipment and transportation equipment to capture images, and the images are converted and synthesized to create a top view (image looking down at the vehicle vertically from above). AVM (Around View Monitoring) technology was also introduced.

However, since monitoring devices such as AVM devices and navigation devices and cameras are all installed in conjunction with a separate distance sensor, there is a problem of high cost. In addition, because expensive AVM devices, navigation devices, and cameras are all fixed or embedded and built into transportation devices, there is a problem that they are only selectively or limitedly applied to expensive heavy equipment or transportation devices. Small or low-cost heavy equipment or transportation devices that do not have separate built-in AVM devices or navigation devices are inevitably exposed to the risk of accidents.

The present invention is intended to solve the above problems, and is a safety assistance device using images that can safely assist the driving and work process of heavy equipment or transportation equipment by applying image capture devices that are easily attached and detachable to heavy equipment or transportation equipment. is to provide.

In addition, it acquires images using video capture devices, detects surrounding objects, obstacles, and nearby workers from the acquired images, and provides a safety assistance device that allows the driver to check the detection results through images and warning sounds. .

The problems of the present invention are not limited to the problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the description below.

A safety assistance device using video according to an example for achieving the above purpose is configured to be detachable from a transportation device and includes at least one video capture device that captures and transmits images in at least one direction, and at least one video capture device. An image processing device that analyzes the patterns and direction of surrounding objects and the positions of objects in the captured video from received video data and generates analysis video data and map data according to the analysis results, and analysis video data and maps from the image processing device. Includes a monitoring module that displays data as video and sound.

At least one image capture device includes an imaging module that captures an image and generates image data for at least one frame, a first communication module that transmits image data of the imaging module wirelessly or wired, and an imaging module and the first communication module. The body of the built-in imaging device may include a detachable member configured to be attached and detached to the transportation device.

Safety assistance devices using images according to embodiments can improve ease of use, frequency of use, and efficiency by safely assisting the driving and work processes of transportation devices using video recording devices that are easily attached and detachable from the exterior of transportation devices. there is.

In addition, it allows drivers to check surrounding objects, obstacles, and nearby workers through videos and warning sounds in real time, thereby preventing safety accidents in advance. In particular, safety assistance efficiency can be further improved by assisting the driver's safety with images according to changes in the working environment due to fog, smog, snow, rain, darkness, etc.

Effects according to the embodiments are not limited to the content exemplified above, and further various effects are included in the present specification.

1 is a configuration diagram showing the arrangement structure of a safety assistance device using images according to an embodiment.
FIG. 2 is a block diagram showing the video capture device shown in FIG. 1.
FIG. 3 is a block diagram showing detailed components of the image processing device shown in FIG. 2.
FIG. 4 is a block diagram specifically showing the image processing unit shown in FIG. 3.
FIG. 5 is a diagram illustrating a method of detecting an image data pattern and direction information by the continuity pattern detector shown in FIG. 4.
FIG. 6 is a diagram showing a block area division method and a pattern analysis method for each block area of the block structure pattern detection unit shown in FIG. 4.
FIG. 7 is a diagram illustrating a method of detecting obstacles for each block area of the block object detector shown in FIG. 4.
FIGS. 8 and 9 are images showing the obstacle detection results for each block area of the block object detector shown in FIG. 4.
FIG. 10 is a block diagram specifically showing the map data processing unit shown in FIG. 3.
FIG. 11 is a diagram showing a map area of a workplace formed through the planar map forming unit of FIG. 10.
Figure 12 is a diagram showing a method of setting a reference location point of a map area.
FIG. 13 is a block diagram specifically showing the composite image processing unit shown in FIG. 3.
FIG. 14 is a diagram showing a blurring area detection block of the blurring area detection unit shown in FIG. 13.
FIG. 15 is a diagram showing a non-detected object block of the non-detected object area detection unit shown in FIG. 13.
FIG. 16 is a diagram showing an alternative image of the alternative image processing unit shown in FIG. 3.

The above-mentioned objects, features, and advantages will be described in detail later with reference to the attached drawings, so that those skilled in the art will be able to easily implement the technical idea of the present invention. In describing the present invention, if it is determined that a detailed description of known technologies related to the present invention may unnecessarily obscure the gist of the present invention, the detailed description will be omitted. Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the attached drawings.

It will be understood that each constituent block and combination of blocks in the drawings can be performed by computer program instructions. These computer program instructions can be mounted on a processor of a general-purpose computer, special-purpose computer, or other programmable data processing equipment, so that the instructions performed through the processor of the computer or other programmable data processing equipment are described in the flow chart block(s). It creates the means to perform functions.

Additionally, the terms '~unit' and '~module' described below refer to software or hardware components such as FPGA (field-programmable gate array) or ASIC (Application Specific Integrated Circuit), and '~unit' and '~module' refer to software or hardware components such as FPGA (field-programmable gate array) or ASIC (Application Specific Integrated Circuit). ~ Modules perform programmed roles. '~ part' and '~ module' are not limited to software or hardware. As an example, '~ part' and '~ module' refer to components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, Includes procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. The functions provided within components and '~parts' and '~modules' can be combined into smaller numbers of components and '~modules' or '~modules' with additional components and '~modules'. ' and '~ modules' can be further separated. Additionally, the components may be implemented to refresh one or more CPUs within the device or secure multimedia card.

Hereinafter, specific embodiments will be described with reference to the attached drawings.

1 is a configuration diagram showing the arrangement structure of a safety assistance device using images according to an embodiment. And, FIG. 2 is a block diagram showing the image capture device shown in FIG. 1.

The safety auxiliary device shown in FIG. 1 includes at least one image capture device (SD1 to SD5), an image processing device (IMP), and a monitoring module (IDM).

Each image capture device (SD1 to SD5) is configured to be attachable to and detachable from a transportation device such as heavy equipment, and captures and transmits images in at least one direction of the transportation device. Each of the video recording devices (SD1 to SD5) may be disposed in the front direction, one side and the other side direction, the rear direction, or the oblique viewing angle direction of the transportation device. Each image capture device (SD1 to SD5) can capture images in the front direction, one side and the other side direction, the back direction, and the oblique viewing angle direction, etc., depending on the arrangement direction, and transmit the captured image data in units of at least one frame.

Referring to FIG. 2, each image capture device (SD1 to SD5) includes a plurality of imaging modules (S1), a first communication module (S2), and a detachable member (S3).

The imaging module S1 captures an image in the front direction using a plurality of imaging elements, converts the analog image signal to digital according to the capturing result, and generates image data in units of at least one frame.

The first communication module (S2) processes digital image data of the imaging module (S1) through a short-range wireless communication module such as Wi-Fi, Bluetooth, or ZigBee, a long-distance wireless communication module such as LTE or 5G, or a wired cable. It is transmitted to a device (IMP), etc.

The detachable member (S3) is formed of a clamp type, tightening clip type, screw type, Velcro type, magnet type, etc., so that the imaging module (S1) and the first communication module (S2) attach the main body of the imaging device (SD1 to SD5). Attach and detach from the transportation device. Using the detachable member S3, the user can easily attach and detach each image capture device (SD1 to SD5) from the exterior of the transportation device and easily change the positions of the image capture devices (SD1 to SD5).

The image processing device (IMP) analyzes the patterns, pattern directions, and positions of surrounding objects in the captured image from the image data received from at least one image capture device (SD1 to SD5), and provides analysis image data according to the analysis results. Create map data.

The image processing device (IMP) stores image data input from at least one image capture device (SD1 to SD5) by dividing them into at least one frame unit according to the shooting direction, and stores the image data in the captured image through the stored image data for each frame. Detects the patterns in which objects are arranged and the directional information of the patterns. At this time, the pattern of objects for each divided block area and the directionality information of the pattern for each block area are detected from the image data for each frame. In particular, by comparing the pattern of objects in at least one frame and the directionality information of the pattern with the pattern of objects in each divided block region and the directionality information of the patterns of objects in each block region, the background objects and obstacles in each block region are identified. Can be distinguished and detected.

Additionally, the image processing device (IMP) generates location coordinate data of the transportation device and places it on a plane to set a map area. Then, map data of the workplace is generated by matching and arranging the pattern of objects for at least one frame and the objects for each block area in the map area. Accordingly, the image processing device (IMP) matches the location coordinate data to the map data of the workplace in real time, generates a composite map image of the map data and the transportation device, and transmits a proximity warning notification message between obstacles, adjacent objects, and the transportation device. can do.

In addition, the image processing device (IMP) detects an object-not-detected area (e.g., blurring area, object-not-detected area, color-deficient area, etc.) from the image data for each frame, and sets the reference position when detecting an object-not-detected area. Video data matching the points can be transmitted to the video display monitor of the monitoring module (IDM).

The monitoring module (IDM) includes an image display monitor that displays analysis image data and map data from the image processing device (IMP) as image and sound. The monitoring module (IDM) includes an image display monitor such as OLED or LCD, and displays image data input from the image processing device (IMP), data of undetected areas, map data, etc. as video and audio. That is, the monitoring module (IDM) displays image data acquired through the image processing device (IMP), obstacle and worker detection data, driver images, warning messages, etc.

FIG. 3 is a block diagram showing detailed components of the image processing device (IMP) shown in FIG. 2.

Referring to FIG. 3, the image processing device (IMP) includes an image processing unit 110, a map data processing unit 120, a composite image processing unit 130, an alternative image processing unit 140, and an image transmission control unit 150.

The image processing unit 110 analyzes the patterns and direction of surrounding objects in the captured image and the positions of the objects from the image data received from at least one image capturing device (SD1 to SD5).

Specifically, the image processing unit 110 stores image data input from at least one image capture device SD1 to SD5 by dividing them into at least one frame unit according to the shooting direction. The image processing unit 110 detects the pattern of the objects in the captured image and the directionality information of the pattern through image data for each frame. At this time, the pattern of objects for each divided block area and the directionality information of the pattern for each block area are detected from the image data for each frame. In particular, the image processing unit 110 compares the pattern of objects for each frame and the directionality information of the pattern with the pattern of objects for each divided block region and the directionality information of the patterns of objects for each block region to create a background for each block region. Objects and obstacles can be distinguished and detected.

The map data processing unit 120 generates location coordinate data of the transportation device and places it on a plane to set a map area. Map data of the workplace is generated by matching and arranging the patterns of objects for at least one frame and the objects for each block area in the map area.

The composite image processing unit 130 matches the location coordinate data to the map data of the workplace in real time to generate a composite map image of the map data and the transportation device, and transmits a proximity warning notification message between obstacles, adjacent objects, and the transportation device.

The alternative image processing unit 140 detects an object-not-detected area (e.g., a blurring area, an object-not-detected area, a color-deficient area, etc.) from the image data for each frame, and sets reference position points when detecting an object-not-detected area. Detect image data that matches.

The video transmission control unit 150 transmits video data matching the reference position points to the monitoring module (IDM), so that the video data matching the reference position points are displayed on the video display monitor of the monitoring module (IDM).

FIG. 4 is a block diagram specifically showing the image processing unit shown in FIG. 3.

Referring to FIG. 4, the image processing unit 110 analyzes the pattern, pattern direction, and location of surrounding objects in the captured image from image data received from at least one image capture device (SD1 to SD5), and provides the analysis result. Generates analysis video data and map data. For this purpose, the image processing unit 110 includes an image frame storage unit 111, a continuity pattern detection unit 113, a block structure pattern detection unit 115, and a block object detection unit 117.

The image frame storage unit 111 stores image data input from at least one image capture device SD1 to SD5 by dividing them into units of at least one frame according to the shooting direction.

FIG. 5 is a diagram illustrating a method of detecting an image data pattern and direction information by the continuity pattern detector shown in FIG. 4.

Referring to FIG. 5, the continuity pattern detection unit 113 analyzes image data for each frame generated and stored through at least one image capture device (SD1 to SD5) to determine the pattern of objects in the captured image. and detecting directional information of the pattern.

Specifically, the continuity pattern detector 113 detects the luminance value and grayscale value of the pixels included in the pattern of the object and the edge area for each object from the image data for at least one frame, and detects the luminance and grayscale values of the pixels included in the edge area for each object. The luminance difference value and grayscale difference value between the two are calculated. Additionally, a continuity pattern is formed by connecting pixels with similar luminance difference values and grayscale difference values between adjacent pixels within a preset error value range. The extension direction of continuity patterns in which similar pixels are connected where the luminance difference value and grayscale difference value between adjacent pixels are within a preset error value range can be set as pattern direction information.

FIG. 6 is a diagram showing a block area division method and a pattern analysis method for each block area of the block structure pattern detection unit shown in FIG. 4.

The block structure pattern detector 115 divides and distinguishes image data for at least one frame into a plurality of preset block areas, and detects the pattern of objects for each divided block area and the directionality information of the pattern for each block area.

Specifically, the block structure pattern detector 115 divides image data for at least one frame into a plurality of preset block areas, and determines the pattern of the object included in each divided block area and the luminance of the pixels included in the edge area for each object. Detect values and grayscale values. Then, the luminance difference value and grayscale difference value between adjacent pixels included in each divided block area are calculated. Additionally, a continuity pattern is formed by connecting pixels with similar luminance difference values and grayscale difference values between adjacent pixels within a preset error value range. The extension direction of continuity patterns in which similar pixels are connected where the luminance difference value and grayscale difference value between adjacent pixels are within a preset error value range can be set as pattern direction information.

FIG. 7 is a diagram illustrating a method of detecting obstacles for each block area of the block object detector shown in FIG. 4.

Referring to FIG. 7, the block object detector 117 includes the pattern of objects for each frame and the directionality information of the pattern, the pattern of objects for each divided block area (BL), and the directionality information of the patterns of objects for each block region. By comparing and contrasting, background objects and obstacles are distinguished and detected for each divided block area (BL).

The block object detector 117 detects or reads the luminance and grayscale values of the display pixels of the background object in each divided block area BL. Then, the luminance and grayscale values of pixels that differ by more than a preset error value range are detected compared to the luminance and grayscale values of the display pixels of the background object. At this time, obstacles (OJDs) can be distinguished and detected by extending pixels that differ by more than a preset error value range.

FIGS. 8 and 9 are images showing the obstacle detection results for each block area of the block object detector shown in FIG. 4.

As described above, the block object detection unit 117 can distinguish obstacles (OJDs) by detecting the luminance and grayscale values of pixels that differ by more than a preset error value range compared to the luminance and grayscale values of the display pixels of the background object. .

The monitoring module (IDM) uses video display monitors such as OLED and LCD to display video data, data of undetected areas, map data, etc. input from the image processing device (IMP) as video and audio. In particular, the monitoring module (IDM) can distinguish obstacles (OJDs) from images of background objects and display them on an image display monitor. Users or drivers of transportation equipment can identify background objects and obstacles (OJDs) around the work site through a video display monitor.

In addition, users or drivers of transportation devices can check image data, obstacle and worker detection data, driver images, warning messages, etc. acquired through an image processing device (IMP) in real time.

FIG. 10 is a block diagram specifically showing the map data processing unit shown in FIG. 3.

Referring to FIG. 10, the map data processing unit 120 sets a map area by arranging the location coordinate data of the transportation device on a plane, and sets a map area with a pattern of objects for at least one frame and objects for each block area. Generate map data of the workplace by matching. That is, the map data processing unit 120 analyzes the pattern and pattern direction of surrounding objects in the captured image and the positions of the objects from the image data received from at least one image capture device (SD1 to SD5) and analyzes the analyzed image data according to the analysis results. and map data can be created.

For this purpose, the map data processing unit 120 includes a location detection unit 121, a planar map forming unit 122, a location point setting unit 123, a map data generating unit 124, and a composite map image data generating unit 125. , and a notification transmission unit 126.

The position detection unit 121 generates location coordinate data of the transport device according to changes in the position of the moving transport device in real time. The location detection unit 121 receives GPS location coordinate data in real time using a GPS coordinate signal detection module and transmits the location coordinate data in real time.

FIG. 11 is a diagram showing a map area of a workplace formed through the planar map forming unit of FIG. 10. And, Figure 12 is a diagram showing a method of setting a reference location point of a map area.

Referring to FIG. 11, the planar map forming unit 122 arranges position coordinate data generated and transmitted in real time on a plane and accumulates the position coordinate data on the plane in real time to set a map area. Since the map area is set according to the movement location, path, and movement radius of the transportation devices detected through GPS location coordinate data, it can be a movement space for the transportation devices without background objects and obstacles.

Referring to FIG. 12, the location point setting unit 123 divides and distinguishes the map area set in the planar map forming unit 122 into blocks of a matrix type, and sets the intersection point of each block of the matrix type as reference location points. You can. That is, as shown in FIG. 12, the location point setting unit 123 divides the movement space of the transportation devices into matrix-type blocks and sets the intersection points of each matrix-type block as reference location points.

The map data generator 124 stores the pattern of objects detected for at least one frame, and images or location information for objects in each block area, at an outer location of the map area according to the reference location points and direction of the map area. Match and place. Specifically, the map data generator 124 generates map data of the workplace by matching patterns of individual objects and obstacles for each block area to reference location points corresponding to the inside of the map area. The map data generator 124 generates map data of the workplace by additionally arranging image data captured at the reference location points, patterns of objects detected at the reference location points, and obstacles around the reference location points. do.

Additionally, the composite map image data generator 125 generates a composite map image of the map data and the transportation device by matching the location coordinate data according to the change in real-time moving location to the map data of the workplace. Accordingly, the notification transmitter 126 matches location coordinate data according to changes in real-time moving location with map data of the workplace in real time, and can transmit a proximity warning notification message between obstacles, adjacent objects, and transportation devices.

FIG. 13 is a block diagram specifically showing the composite image processing unit shown in FIG. 3. And FIG. 14 is a diagram showing the blurring area detection block of the blurring area detection unit shown in FIG. 13.

Referring to FIGS. 13 and 14 , the composite image processing unit 130 includes a blurring area detection unit 131, an object-not-detected area detection unit 133, a color-deficient area detection unit 135, and a point matching unit 137. .

The blurring area detection unit 131 detects a blurring area for each divided block area by comparing changes in luminance value or grayscale difference between pixels included in each divided block area BL.

Specifically, the blurring area detection unit 131 detects the luminance value and grayscale value of pixels included in the pattern of the object and the edge area for each object from the image data for each divided block area detected by the block structure pattern detection unit 115. Then, the luminance difference value and grayscale difference value between adjacent pixels included in the edge area for each object are calculated and stored.

The blurring area detection unit 131 compares the luminance difference values between adjacent pixels included in the edge areas of the patterns on at least one frame basis, and determines the luminance difference values between the pixels included in the edge areas according to the first and second standards. If it is included in the range between the luminance difference values, the divided block area is set as a blurring area. Additionally, if the grayscale value between adjacent pixels included in the edge area of the patterns falls within the range between the first and second reference grayscale difference values, the divided block area may be set as a blurred area.

The blurring area is an area where the image is detected as blurred due to environmental effects such as fog and smog, and may be an area where detection of surrounding objects or obstacles is impossible.

The object undetected area detection unit 133 detects object undetected block areas in units of a plurality of divided block areas by comparing changes in the luminance average value or grayscale average value difference of the pixels included in each divided block area BL.

FIG. 15 is a diagram showing a non-detected object block of the non-detected object area detection unit shown in FIG. 13.

Referring to FIG. 15, the object undetected area detection unit 133 detects the luminance average value or grayscale average value of the image data for each divided block area BL detected by the block structure pattern detection unit 115, and detects the luminance average value or grayscale average value of each adjacent block area. The luminance average difference value or grayscale average difference value for each divided block area is calculated and stored. And the object undetected area detection unit 133 compares the luminance average difference value for each adjacent divided block region, so that the luminance average difference value between each adjacent divided block region is between the first and second luminance reference average values. If it is included in the range, the comparison partition area can be set to the object undetected block area. In addition, the object undetected area detection unit 133 compares the gray level average difference value for each adjacent divided block area, so that the gray level average difference value between each adjacent divided block area is equal to the first and second gray level reference average values. If it is included in the range, the comparison partition area can be set to the object undetected block area.

The object-not-detected block area is an area where objects are not detected because the front image is partially obscured by weather environmental effects such as snow, rain, fog, frost, or fog, and may become an area where detection of surrounding objects or obstacles is impossible.

The color tone missing area detection unit 135 compares the luminance average value or grayscale average value of the pixels included in each divided block area with a preset low brightness reference value or low grayscale reference value, and determines the low brightness or low grayscale value in units of a plurality of divided block areas. Detect grayscale block areas.

The color tone missing area detection unit 135 detects the average luminance value or grayscale average value of the image data for each divided block area detected by the block structure pattern detection unit, and compares the average luminance value for each divided block area with a preset low brightness reference value. do. At this time, if the luminance average value of the block area is less than the preset low luminance reference value, the color tone missing area detection unit 135 sets the block area whose luminance average value is smaller than the preset low luminance reference value as the color tone missing area. On the other hand, if the grayscale average value of the block area is smaller than the preset low grayscale reference value, the color tone missing area detection unit 135 may set the block area whose grayscale average value is smaller than the preset low grayscale reference value as the color tone missing area.

A color-deficient area is an area where images are detected only at a certain brightness and objects are not detected due to accidental conditions such as snow or darkness, and may become an area where detection of surrounding objects or obstacles is impossible.

The point matching unit 137 compares the location coordinates of the location coordinate data generated in real time with the reference location points of the map area, and generates a matching code and coordinate information for the matching points at the time when the location coordinates and the reference location points are matched. do. As shown by the reference point positions in FIG. 12, when the location coordinates of the transportation device and the reference location points are matched, matching codes and coordinate information of the matching points are generated at the matched time and location and sent to the alternative image processor 140. send.

FIG. 16 is a diagram showing an alternative image of the alternative image processing unit shown in FIG. 3.

Referring to FIG. 16, the alternative image processing unit 140 displays a composite map image of the transportation device according to the map data on the video display monitor at the time a blurring area, an object-not-detected area, a color-deficient area, etc. are detected. And, image data matched to the reference position points or matching points are additionally displayed on the image display monitor. Accordingly, drivers of transportation devices can move to a safe location by checking the image matched to the map area, map information, and matching points of the video display monitor even in situations where visibility is limited. for teeth. The alternative image processing unit 140 includes a matching image storage unit 142 and an alternative image display control unit 144.

The matching image storage unit 142 captures images in real time from at least one video capture device (SD1 to SD5) at each matching time point at which the location coordinates of the transportation device are matched with the reference location points based on the matching code and coordinate information of the matching points. The matching code is matched with the image data and stored in memory.

The alternative image display control unit 144 transmits the composite map image of the transportation device to the video display monitor when object-not-detected block areas, low-brightness or low-gradation color tone missing areas, or blurring areas occur. In addition, the image matched with the matching code at each point in time when the location coordinates of the transportation device are matched with the reference location points as a result of the occurrence of block areas where objects are not detected, areas lacking low-brightness or low-gradation color, or blurring areas. Data is transmitted to the video display monitor of the monitoring module (IDM).

The embodiments of the present invention disclosed in the specification and drawings are merely provided as specific examples to easily explain the technical content of the present invention and to facilitate understanding of the present invention, and are not intended to limit the scope of the present invention. In addition, the embodiments according to the present invention described above are merely illustrative, and those skilled in the art will understand that various modifications and equivalent scopes of the embodiments are possible therefrom. Therefore, the true technical protection scope of the present invention should be determined by the following patent claims.

110: Image processing unit
111: Video frame storage unit
120: Map data processing unit
121: Position detection unit
122: Location point setting unit
130: composite image processing unit
131: Blurring area detection unit
140: Alternative image processing unit
150: Video transmission control unit

Claims (5)

At least one video capture device that captures and transmits images from the transportation device;
An image processing device that generates analysis image data and map data according to the arrangement direction and location of objects in an image captured by at least one image capture device; and
Comprising a monitoring module that displays the analyzed image data and the map data as image and sound,
The image processing device is
an image processing unit that analyzes patterns of the objects, their directions, and pattern arrangement positions from image data for at least one frame, and generates the analyzed image data and the map data according to the analysis results; and
An image including a map data processing unit that generates location coordinate data of the transportation device and arranges it on a plane to set a map area, and generates map data of the workplace by matching the pattern and pattern direction of the objects to the map area. Safety aids used.
According to claim 1,
The image processing device is
Generates a composite map image of the map data by matching the location coordinate data of the object, obstacle, and transportation device to the map data of the workplace in real time, and transmits a proximity warning notification message between the object, the obstacle, and the transportation device. A safety assistance device using an image further comprising a composite image processing unit.
According to claim 1,
The image processing device is
an alternative image processing unit that detects image data matching reference location points from stored image data when detecting a non-detected object area according to the results of analyzing the locations of the objects; and
A safety assistance device using video, further comprising an video transmission control unit that transmits video data matching the reference position points to the monitoring module and displays them on an video display monitor.
According to claim 1,
The image processing unit
a continuity pattern detection unit that detects a pattern in which objects are arranged and directional information of the pattern from the image data for each frame;
a block structure pattern detection unit that divides the image data for each frame into a plurality of preset block areas and detects a pattern of objects for each divided block area and directional information of the pattern for each block area; and
By comparing and contrasting the pattern of objects in each divided block region and the directionality information of the patterns of objects in each block region with the pattern of objects and the directionality information of the pattern in the image data for each frame, a background for each block region is created. A safety assistance device using images that includes a block object detection unit that distinguishes and detects objects and obstacles.
According to claim 1,
The map data processing unit
a position detection unit that generates location coordinate data of the transport device according to changes in the position of the transport device moving in real time;
a plane map forming unit that arranges the position coordinate data on a plane and accumulates the position coordinate data on the plane in real time to set a map area;
a location point setting unit that divides the map area into matrix-type blocks and sets intersections of each matrix-type block as reference location points; and
The pattern of objects for each frame, the objects for each block area are matched and arranged at an outer position of the map area according to the direction of the map area, and the patterns of individual objects and obstacles for each block area. A safety assistance device using an image including a map data generator that generates map data of a workplace by matching the reference location points corresponding to the inside of the map area.