CN112541413B - Dangerous behavior detection method and system for forklift driver real operation assessment and coaching - Google Patents
Dangerous behavior detection method and system for forklift driver real operation assessment and coaching Download PDFInfo
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- B66F9/00—Devices for lifting or lowering bulky or heavy goods for loading or unloading purposes
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Abstract
The invention discloses a dangerous behavior detection method and system for forklift driver real operation assessment and coaching, comprising the following steps: installing an industrial camera at the front end of a forklift, adjusting the angle and focal length of the camera until the waist safety belt and the upper body part of a forklift driver can be clearly shot, and recording the adjusted angle of the industrial camera; according to the position and the pose of the forklift, grabbing the position image, acquiring the pixel coordinates of the pose of the forklift by using Mask R-CNN target identification and an image segmentation frame, and calculating the pose angle of the upper body of the driver; according to the imaging structure of the camera system, the proportion of the pixel spacing to the actual spacing is obtained, and dangerous behavior judgment standards are calculated; the industrial camera shoots and monitors the gesture of a forklift driver, detects whether a safety belt is tied, recognizes the gesture pixel coordinates of the driver by using a Mask R-CNN, calculates a safety score, and judges whether the behavior is dangerous. The intelligent recognition and detection device and the intelligent recognition and detection method realize intelligent recognition and detection of dangerous behaviors of the driver in the forklift driver real-operation assessment and coaching processes.
Description
Technical Field
The invention relates to the field of forklift driver assessment and coaching, in particular to a dangerous behavior detection method and system for forklift driver real-operation assessment and coaching based on deep learning.
Background
The forklift is widely applied to ports, stations, airports, goods yards, factory workshops, warehouses, circulation centers, distribution centers and the like, and is an indispensable device for pallet transportation and container transportation when carrying out pallet cargo loading and unloading operations in cabins, carriages and containers. The actual operation assessment and coaching quality of forklift drivers are related to engineering efficiency and engineering safety. The method relates to a deep learning method, can detect dangerous behaviors of a forklift driver in the process of actual operation and examination and coaching, is beneficial to industry improvement of teaching and training quality of the forklift driver, standardizes driving and operating behavior habits of the forklift driver, reduces the probability of occurrence of safety accidents of the forklift from the source, and powerfully guarantees the use safety of the forklift.
In the prior art of the dangerous behavior detection method and system for the forklift driver real operation assessment and coaching, the invention comprises the following comparative patents and documents:
1) A public traffic driver offence detection system (CN 109376634A) based on a neural network discloses a public traffic driver offence detection system, which collects various behavior videos of a driver to judge by adopting a neural network method. The method is distinguished from the deep learning method adopted by the invention by adopting a neural network, and the dangerous behavior judgment of the method utilizes calculation to obtain a judgment result.
2) A method and a device for identifying the behavior of a locomotive driver (CN 106941602B) disclose a method and a system for identifying the behavior of the locomotive driver, which predefine multiple classes of behaviors of the driver, and identify the daily operations of the driver by adopting a deep learning algorithm. The invention adopts a Mask R-CNN deep learning method to detect the body posture of a driver and judges the danger through calculation.
3) The invention discloses a swimming pool deep water area early dangerous behavior detection method (CN 111368743A) based on a monitoring video, which is used for detecting that the head of a swimmer is in a swimming state or an upright state by collecting the swimming video. The invention adopts a Mask R-CNN method to detect the upper body of a forklift driver and calculates and judges dangerous states.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a dangerous behavior detection method and system for forklift driver real operation assessment and coaching.
The aim of the invention is achieved by the following technical scheme:
the dangerous behavior detection method for the forklift driver real operation assessment and coaching comprises the following steps:
the industrial camera is arranged at the front end of the forklift, the angle and the focal length of the industrial camera are adjusted until the waist safety belt and the upper body part of a forklift driver can be clearly shot, and the adjusted angle of the industrial camera is recorded;
b, capturing the position image according to the pose of the forklift driver, acquiring the pixel coordinates of the pose of the forklift driver by using Mask R-CNN target recognition and an image segmentation frame, and calculating the pose angle of the upper body of the driver;
c, obtaining the proportion of the pixel spacing to the actual spacing according to the imaging structure of the camera system, and calculating a dangerous behavior judgment standard;
and D, shooting and monitoring the gesture of a forklift driver by an industrial camera, detecting whether a safety belt is tied, identifying the gesture pixel coordinates of the driver by using a Mask R-CNN, calculating a safety score, and judging whether the behavior is dangerous.
Dangerous behavior detection method and system for forklift driver real operation assessment and coaching, comprising the following steps: industrial camera, industrial camera fixture, upper computer, loudspeaker; the said
The industrial camera is used for capturing an image of the gesture position of the driver and uploading the image to the upper computer;
the industrial camera clamp is used for installing an industrial camera and adjusting an angle-fixed camera;
the upper computer is used for identifying the captured driver posture position image and calculating and judging whether the driver is in dangerous behavior;
and the loudspeaker is used for playing reminding audio after the dangerous behavior of the driver is detected.
One or more embodiments of the present invention may have the following advantages over the prior art:
the industrial camera, the upper computer and the loudspeaker are adopted, so that the gesture image of the forklift driver can be captured in real time, the pixel coordinate information of the position where the forklift driver is located can be identified and calculated through the upper computer, the behavior of the driver is detected and judged, and the forklift driver is broadcasted through the loudspeaker. The method has the advantages of high automation degree, high speed and high alignment precision, can be applied to detection and reminding of dangerous behaviors of a forklift driver in the actual operation and examination and coaching processes, and has practical significance and popularization value.
By using the Mask R-CNN target recognition and image segmentation method, the dangerous behavior of the driver in the actual operation and examination and coaching processes of the forklift driver is intelligently recognized and detected.
Drawings
FIG. 1 is a flow chart of a dangerous behavior detection method for a forklift driver real-time operation assessment and coaching;
FIG. 2 is a flow chart of a dangerous behavior detection method for a forklift driver to perform real operation assessment and coaching.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the following examples and the accompanying drawings.
As shown in FIG. 1, the dangerous behavior detection method for the forklift driver real operation assessment and coaching comprises the following steps:
step 10, installing an industrial camera at the front end of a forklift, adjusting the angle and focal length of the camera until the waist safety belt and the upper body part of a forklift driver can be clearly shot, and recording the adjusted angle of the industrial camera;
step 20, according to the pose of the forklift truck driver, grabbing the position image, acquiring the pixel coordinates of the pose of the forklift truck driver by using Mask R-CNN target identification and an image segmentation frame, and calculating the pose angle of the upper body of the driver;
step 30, according to the imaging structure of the camera system, obtaining the proportion of the pixel spacing to the actual spacing, and calculating a dangerous behavior judgment standard;
and 40, shooting and monitoring the gesture of a forklift driver by using an industrial camera, detecting whether a safety belt is well tied, identifying the gesture pixel coordinates of the driver by using a Mask R-CNN, calculating a safety score, and judging whether the behavior is dangerous.
As shown in fig. 2, the adjusted industrial camera lens axis direction determined in step 10 is at an angle α to the gravitational direction.
As shown in fig. 2, the method for calculating the upper body pose angle of the driver in the step 20 is as follows:
let N be 0 Number of pixels for body boundary of driver's seat belt, (x) 0 ,y 0 ,z 0 ) Reference is made to the reference coordinates for the belt portion. With the body boundary of the safety belt of the driver as a reference coordinate set, { (x) 0i ,y 0i ,z 0i )|i＝1,2,……,N 0 And calculate the belt part reference coordinates:
let N be 1 Number of pixels for body boundary of driver's seat belt, (x) 1 ,y 1 ,z 1 ) Reference is made to the reference coordinates for the belt portion. To identify the driver's shoulder pose and store the set of pixel coordinates where it is located, we can get { (x) 1i ,y 1i ,z 1i )|i＝1,2,……,N l -and calculate shoulder reference coordinates: ,
the position and the shoulder reference coordinates of the safety belt can be used for calculating an angle beta formed by the pose of the driver and the vertical upward direction:
as shown in fig. 2, the above step 30 determines that the dangerous behavior safety criteria are:
according to the system structure and the actual distance delta s corresponding to one pixel spacing on the camera imaging surface l 、Δs s The risk factor is ζ, so the judgment coefficient A is calculated as follows:
wherein A is 1 、A 2 、A 3 There is a constraint relationship as follows:
A 1 +A 2 +A 3 ＝1
in step 40, as shown in fig. 2, it is determined whether the behavior is dangerous as follows:
and (3) recognizing the gesture behavior of the driver, if the driver is recognized as being tied with the safety belt, directly judging that the driver is in dangerous behavior, and if the driver is tied with the safety belt, continuing to detect and judge.
Set S t1 、S t2 、S t3 Score for driver gesture behavior to identify the initial gesture of the driver and store the pixel coordinate set where it is located, get { (x) 0j ,y 0j ,z 0j )|j＝1,2,……,M 0 Mask R-CNN recognizes the driver's posture pixel coordinates, and { (x) can be obtained 1j ,y 1j ,z 1j )|j＝1,2,……,M 0 -driver gestural behavioural risk score can be calculated:
according to the scoring condition of the driver, whether the driver is in a dangerous state or not can be judged:
if the detection and judgment result shows that the driver is in dangerous behavior, the loudspeaker broadcasts danger and notices safety audio.
Although the embodiments of the present invention are described above, the embodiments are only used for facilitating understanding of the present invention, and are not intended to limit the present invention. Any person skilled in the art can make any modification and variation in form and detail without departing from the spirit and scope of the present disclosure, but the scope of the present disclosure is still subject to the scope of the appended claims.
Claims (4)
1. The dangerous behavior detection method for the forklift driver real operation assessment and coaching is characterized by comprising the following steps:
the industrial camera is arranged at the front end of the forklift, the angle and the focal length of the industrial camera are adjusted until the waist safety belt and the upper body part of a forklift driver can be clearly shot, and the adjusted angle of the industrial camera is recorded;
b, capturing a position image of the position of the driver according to the position of the driver of the forklift, acquiring pixel coordinates of the position of the driver of the forklift by using Mask R-CNN target identification and an image segmentation frame, and calculating the position and the angle of the upper body of the driver;
c, obtaining the proportion of the pixel spacing to the actual spacing according to the imaging structure of the camera system, and calculating a dangerous behavior judgment standard;
d, shooting and monitoring the gesture of a forklift driver by an industrial camera, detecting whether a safety belt is tied, identifying the gesture pixel coordinates of the driver by using a Mask R-CNN, calculating a safety score, and judging whether the behavior is dangerous;
the method for calculating the pose angle of the upper body of the driver in the step B comprises the following steps:
let the number of pixels at the body boundary of the safety belt of the driver be N 0 The belt portion reference coordinate is (x 0 ,y 0 ,z 0 ) The method comprises the steps of carrying out a first treatment on the surface of the With the body boundary of the safety belt of the driver as a reference coordinate set, get { (x) 0i ,y 0i ,z 0i )|i＝1,2,……,N 0 And calculate the belt part reference coordinates:
let N be 1 Number of pixels for body boundary of driver's seat belt, (x) 1 ,y 1 ,z 1 ) Reference coordinates for the seat belt portion; to identify the driver's shoulder pose and store the set of pixel coordinates where it is located, get { (x) 1i ,y 1i ,z 1i )|i＝1,2,……,N l -and calculate shoulder reference coordinates:
calculating an angle beta formed by the pose of the driver and the vertical upward direction according to the reference coordinates of the position of the safety belt and the reference coordinates of the shoulder:
2. the dangerous behavior detection method for forklift driver real operation assessment and coaching according to claim 1, wherein in the step a, the adjusted industrial camera angle is an angle formed by the lens axis direction and the gravity direction, and the angle is alpha.
3. The dangerous behavior detection method for the forklift driver real operation assessment and coaching of claim 2, wherein the dangerous behavior safety standard judging in the step C is as follows:
according to the system structure and the actual distance delta s corresponding to one pixel spacing on the camera imaging surface l 、Δs s The risk factor is ζ, so the judgment coefficient A is calculated as follows:
wherein A is 1 、A 2 、A 3 There is a constraint relationship as follows:
A 1 +A 2 +A 3 ＝1。
4. the dangerous behavior detection method for forklift driver real operation assessment and coaching according to claim 3, wherein in the step D, it is determined whether the behavior is dangerous as follows:
recognizing the gesture behavior of the driver, if the driver is recognized to be unbuckled, directly judging that the driver is in dangerous behavior, and if the safety belt is unbuckled, continuing to detect and judge;
set S t1 、S t2 、S t3 Score for driver gesture behavior; to identify the initial pose of the driver and store the set of pixel coordinates where it is located, to get { (x) 0j ,y 0j ,z 0j )|j＝1,2,……,M 0 Mask R-CNN recognizes the driver's attitude pixel coordinates to get { (x) 1j ,y 1j ,z 1j )|j＝1,2,……,M 0 -calculating a driver gesture behavior risk score:
judging whether the driver is in a dangerous state according to the scoring condition of the driver:
if the detection and judgment result shows that the driver is in dangerous behavior, the loudspeaker broadcasts danger and notices safety audio.
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