CN108876765A - The target locating set and method of industrial sorting machine people - Google Patents
The target locating set and method of industrial sorting machine people Download PDFInfo
- Publication number
- CN108876765A CN108876765A CN201810496379.8A CN201810496379A CN108876765A CN 108876765 A CN108876765 A CN 108876765A CN 201810496379 A CN201810496379 A CN 201810496379A CN 108876765 A CN108876765 A CN 108876765A
- Authority
- CN
- China
- Prior art keywords
- network
- layer
- training
- target
- sorting machine
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
- G06F18/24147—Distances to closest patterns, e.g. nearest neighbour classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
- G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Probability & Statistics with Applications (AREA)
- Quality & Reliability (AREA)
- Image Analysis (AREA)
Abstract
The present invention relates to a kind of target locating set for sorting machine people, it further includes the program for executing following steps which, which includes camera, processor and memory,：The pictorial information of object to be measured is obtained by camera；Load convolutional neural networks frame and its training pattern；Using the anchor boxes of several candidates, bounding box is generated in characteristic pattern；Predict the corresponding centre coordinate of bounding box and confidence score；The maximum bounding box of confidence score is obtained by non-maxima suppression.The program that the training pattern of the load convolutional neural networks is the steps of：Carry out the pre-training of network；Obtain the label data collection of target object；Clustering is carried out to the target frame that label data is concentrated；The network output that each layer is obtained by propagated forward, obtains the error of output with label；According to the gradient of error retrospectively calculate each layer weight and biasing, and adjust weight and the biasing of each layer.Present invention detection speed is very fast, and while guaranteeing precision, real-time may be implemented.
Description
Technical field
The present invention relates to target locating set and the sides of a kind of industrial sorting machine people, more particularly to industrial sorting machine people
Method.
Background technique
Nowadays, automated production is more more and more universal, and robot is widely applied on various industrial flow-lines, and
Workpiece mechanical sorting operation is a common task in industrial flow.It is sorting operation to identifying and positioning for moving target
Basis, the method that the sorting machine National People's Congress of early stage mostly uses teaching to program, though the operation of achievable some fixations, is unable to complete
More intelligentized operation；And general machine vision technique, such as simple edge extracting, template matching, image enhancement etc.
Processing technique is difficult in complex scene accurately to sort target object although being capable of detecting when target object.
Convolutional neural networks（CNN）It is mainly used to the X-Y scheme of identification distortion invariance, it is to simulate animal brain
A kind of mathematical model, as long as being trained with known mode to convolutional network, it can learn largely input with it is defeated
Mapping relations between out, with the mapping ability between inputoutput pair.Convolutional neural networks algorithm is used for sorting machine
People, when chaff interferent is more, density is larger in scene, needs although being able to solve the demand of the sorting precision in complex scene
Multiple target objects are handled simultaneously, and be easy to be influenced by factors such as illumination, the speed of target identification and positioning
It is relatively slow, it is easy to produce missing inspection and erroneous detection, requirement when being unable to satisfy the handgrip sorting of machine end for real-time.
This is the deficiencies in the prior art place.
Summary of the invention
In view of the deficiencies of the prior art, the technical problem to be solved in the present invention is to provide the mesh of industrial sorting machine people a kind of
Position device and method are demarcated, they may help to realize the real-time detection to target object.
The target locating set of industrial sorting machine people of the invention, including camera, processor and memory, feature
It is：The device further includes the program for executing following steps：
The pictorial information of object to be measured is obtained by camera；
Convolutional neural networks frame and its training pattern are loaded, the anchor boxes of several candidates is obtained；
Using anchor boxes, bounding box is generated in characteristic pattern；
Corresponding wide, high, centre coordinate and confidence score are predicted to above-mentioned several bounding boxes；
The maximum bounding box of confidence score is obtained by non-maxima suppression, corresponding centre coordinate is the position of target.
The program that the training pattern of the load convolutional neural networks is the steps of：
The pre-training that network is carried out first on ImageNet data set, obtains the pre-training model of network；
Then the data set that target object is obtained by camera, is manually marked, and label data collection is obtained；
To the width and high pass k-means progress clustering of the target frame that label data is concentrated, obtain several groups of candidates'
anchor boxes；
The network output that each layer is obtained by propagated forward, obtains the error of output with label；
The gradient of each layer weight and biasing is reversely successively calculated according to error, and adjusts weight and the biasing of each layer.
K=2.
It in pre-training, is trained using multiple dimensioned, changes the size of input picture every 10 wheels.
In pre-training, using 32 as the down-sampling factor, it is maintained at the size for inputting picture between 320 to 608.
The convolutional neural networks are improvement on the basis of Darkne-19, eliminate the last one convolutional layer, are increased
3 convolution kernel sizes are 3x3, convolutional layer that the convolutional layer and a convolution kernel size that port number is 1024 are 1x1.
The object localization method of industrial sorting machine people of the invention, it is characterized in that including the following steps：
The pictorial information of object to be measured is obtained by camera；
Convolutional neural networks frame and its training pattern are loaded, the anchor boxes of several candidates is obtained；
Using anchor boxes, bounding box is generated in characteristic pattern；
Corresponding wide, high, centre coordinate and confidence score are predicted to above-mentioned several bounding boxes；
The maximum bounding box of confidence score is obtained by non-maxima suppression, corresponding centre coordinate is the position of target.
The training pattern of the load convolutional neural networks is the steps of：
The pre-training that network is carried out first on ImageNet data set, obtains the pre-training model of network；
Then the data set that target object is obtained by camera, is manually marked, and label data collection is obtained；
To the width and high pass k-means progress clustering of the target frame that label data is concentrated, obtain several groups of candidates'
anchor boxes；
The network output that each layer is obtained by propagated forward, obtains the error of output with label；
The gradient of each layer weight and biasing is reversely successively calculated according to error, and adjusts weight and the biasing of each layer.
The beneficial effects of the invention are as follows：First, the model trained using convolutional neural networks can be right in complex scene
It is positioned in target, due to selecting picture in its entirety to carry out training pattern, the approach of entire target detection is a single convolution
Neural network, detection performance can be optimized end to end, therefore detection speed is very fast, can while guaranteeing precision
To realize the requirement of real-time.Second, convolutional neural networks of the invention be on the basis of basic network Draknet-19 into
Capable improvement, being added to 3 convolution kernels is 3*3, and the convolutional layer of channel numerical digit 1024,1 convolution kernel is the convolutional layer of 1*1, is added
While deep network, reduce the training parameter of network, enables the network to extract characteristic information more abundant, improve network
To the precision of target object identification.Third, due to carrying out multiple dimensioned training to network, network has various sizes of input picture
There is robustness.Finally, the statistical law of bounding box is found by concentrating the bounding box marked by hand to do clustering data,
It was found that there is preferable sorting effect as k=2.
Detailed description of the invention
Fig. 1 is the flow chart of the target detection of industrial sorting machine people of the invention.
Fig. 2 is the convolutional neural networks training stage flow chart of industrial sorting machine people of the invention.
Fig. 3 is the network structure table of the convolutional neural networks of industrial sorting machine people of the invention.
Fig. 4 is the number of iterations of convolutional neural networks training of the invention and the relational graph of loss function.
Fig. 5 is the k value and cost function plots of industrial sorting machine people of the invention.
Specific embodiment
Now in conjunction with drawings and examples, invention is further described in detail.
The present invention applies to improved YOLOv2 convolutional neural networks algorithm in the sorting machine people of processing part, realizes
Sort operation based on machine vision.The system obtains pictorial information by the camera on conveyer belt, from different model
Target part is identified and positioned out in part in real time, and is grabbed by the handgrip of mechanical end, realizes neat put.
Referring to Fig. 1, Fig. 2, YOLOv2 convolutional neural networks algorithm is located target detection problems as a regression problem
Reason, can disposably predict position and the classification of multiple target frames in real time, be completed at the same time target object in one network
Positioning and classification, entire detection process is monolithic.
Due to generating bounding box by grid, recall rate is lower, and YOLOv2 leads to k-means method and concentrates by hand to data
The bounding box of mark does clustering, finds the statistical law of bounding box, with these types of wide and high anchor boxes in feature
Bounding box is generated in figure, improves the accuracy of recall rate and positioning.
For a target object there may be multiple bounding boxes, each bounding box include x, y, h, w and confidence score and
The variables such as classification score；Wherein x, y indicate that the centre coordinate of the bounding box relative to coordinate origin, w, h are indicated relative to whole picture
The width and height of the bounding box of image；Confidence score has reacted the accuracy of bounding box prediction；
Need to leave the highest bounding box of confidence score by non-maxima suppression, to realize the accurate positioning to object；
It is input in softmax classifier by the characteristic information for extracting convolutional layer, each bounding box is predicted
The class probability of multiple objects leaves the highest prediction of class probability score also by non-maxima suppression, to realize to object
Classification；
YOLO v2 convolutional neural networks algorithm is used for the sorting of machined part, due to not having to select sliding window or mention
The mode training network of candidate region is taken, but directly picture in its entirety is selected to carry out training pattern, the approach of entire target detection is
One single convolutional neural networks, detection performance can be optimized end to end, therefore detection speed is very fast, is guaranteeing
While precision, the requirement of real-time may be implemented.
Since label data is less, and often, resolution ratio is lower, if the label data collection for directlying adopt production carries out net
The training of network, often precision is not high, and positioning is poor, therefore in actual training, using the method for pre-training.ImageNet
Data set has more than 1,400 ten thousand width pictures, covers a classification more than 20,000, and image clearly, resolution ratio is higher, and most pictures is with bright
Therefore true classification markup information carries out pre-training on ImageNet data set, obtain the pre-training model of network, make net
Network obtains the generally understanding to target object；
Then the data set that target object is obtained by camera, is manually marked, and label data collection is obtained；For example, by using me
Collect 1000 manual labels nut and shim data collection retraining is carried out to network, then to the width of label data collection
Clustering is carried out using k-means with height, optimal k value is obtained by the relationship between k value and cost function, such as institute in Fig. 5
Show, when k=2, sorting effect is preferable.Then clustering is carried out using optimal k value, obtains several groups of anchor boxes.
The training stage of network is broadly divided into propagated forward and backpropagation, and propagated forward mainly successively calculates each layer
Output valve, backpropagation are mainly the gradient that foundation error reversely successively calculates each layer weight and biasing, and after calculating,
Adjust weight and the biasing of each layer.It is exported in the training process of network by the network that propagated forward obtains each layer first,
Then the error for obtaining output with label, by backpropagation, continuous undated parameter reduces loss function, until network is complete
Convergence.By visualizing to the loss function in convolutional neural networks training process, whether analysis model training restrains, essence
Whether degree reaches preset requirement.
Referring to Fig. 4, when trained the number of iterations reaches 40000 times, training pattern has restrained, at this time average loss letter
Number reaches 1.0.The model trained can accurately identify and position target object in complex scene.
The model trained has reached the speed of 20 frame per second on a moving belt, meets requirement of real-time.Pass through individual
The test of picture is seen, for the picture containing target object arbitrarily inputted, the model trained by convolutional neural networks,
Target object can be detected in complex scene, and can accurately carry out classification and location prediction.
In order to make network that there is certain robustness to the pictures of different input sizes, using the method for multiple dimensioned training,
Every training 10 is taken turns, and the size of input picture is just changed.In the training process, using 32 as the down-sampling factor, make to input picture
Size is maintained between 320 to 608.
Referring to Fig. 3, network structure of the invention uses convolutional neural networks（CNN）It realizes, CNN simulates spy by convolution
Sign is distinguished, and the weight for passing through convolution is shared and pond, to reduce the order of magnitude of network parameter, finally by traditional neural net
Network completes the tasks such as classification.Basic model Darkne-19 based on YOLO has 19 convolutional layers, and first layer is convolutional layer, leads to
Road number is 32, and convolution kernel size is 3x3；The second layer is maximum pond layer, and convolution kernel size is 2x2, step-length 2；Third layer is
Convolutional layer, port number 64, convolution kernel size are 3x3；4th layer is maximum pond layer, and convolution kernel size is 2x2, step-length 2；
Five, the six, seven layers are convolutional layer, and convolution kernel size is respectively 3x3,1x1,3x3, and port number is respectively 128,64,128；8th
Layer is maximum pond layer, and convolution kernel size is 2x2, step-length 2；Nine, the ten, 11 be convolutional layer, and convolution kernel size is respectively
3x3,1x1,3x3, port number are respectively 256,128,256；Floor 12 is great Chiization layer, and convolution kernel size is 2x2, step-length
It is 2；13rd, 14,15,16,17 layer is convolutional layer, and convolution kernel size is respectively 3x3,1x1,3x1,1x1,3x3,
Port number is 512,256,512,256,512；18th layer is maximum pond layer, and convolution kernel size is 2x2, step-length 2；Tenth
Nine, 20,21,22,23 layers are convolutional layer, and convolution kernel size is respectively 3x3,1x1,3x3,1x1,3x3, are led to
Road number is respectively 1024,512,1024,512,1024；24th layer is convolutional layer, and convolution kernel size is 1x1, and port number is
1000.The cascade structure of convolution is largely used in Darkne-19, the size of convolution kernel mainly includes 3 × 3 and 1 × 1 liang
The convolution kernel of kind size.The thought for having used for reference Network in network is all added to 1x1's between the convolution kernel of 3x3
Convolution kernel, wherein convolutional layer is responsible for extracting the characteristic information of target object, and maximum pond layer is to the key message in target object
It extracts, reduces redundancy, reduce the parameter of network training.
It is improved on the basis of Darkne-19 herein, eliminates the last one convolutional layer, increase 3 convolution kernels
Size is 3x3, the convolutional layer that the convolutional layer and a convolution kernel size that port number is 1024 are 1x1.While deepening network,
The training parameter for reducing network enables the network to extract characteristic information more abundant, improves network and knows to target object
Other precision.Using anchor boxes on characteristic pattern predicted boundary frame.
The above is only presently preferred embodiments of the present invention, not does limitation in any form to the present invention, though
So the present invention has been disclosed as a preferred embodiment, and however, it is not intended to limit the invention, any technology people for being familiar with this profession
Member, in the range of not departing from technical solution of the present invention, when the technology contents using the disclosure above make a little change or repair
Decorations are the equivalent embodiment of equivalent variations, but anything that does not depart from the technical scheme of the invention content, technology according to the present invention are real
Matter any simple modification, equivalent change and modification to the above embodiments, still fall within the range of technical solution of the present invention
It is interior.
Claims (8)
1. a kind of target locating set of industry sorting machine people, including camera, processor and memory, it is characterized in that：It should
Device further includes the program for executing following steps：
The pictorial information of object to be measured is obtained by camera；
Load convolutional neural networks frame and its training pattern；
Using the anchor boxes of several candidates, bounding box is generated in characteristic pattern；
Predict corresponding wide, the high and centre coordinate of bounding box and confidence score；
The maximum bounding box of confidence score is obtained by non-maxima suppression, corresponding centre coordinate is the position of target.
2. the target locating set of industry sorting machine people as described in claim 1, it is characterized in that：The load convolutional Neural
The program that the training pattern of network is the steps of：
The pre-training that network is carried out on ImageNet data set, obtains the pre-training model of network；
The data set that target object is obtained by camera, is manually marked, and label data collection is obtained；
To the width and high pass k-means progress clustering of the target frame that label data is concentrated, obtain several groups of candidates'
anchor boxes；
The network output that each layer is obtained by propagated forward, obtains the error of output with label；
The gradient of each layer weight and biasing is reversely successively calculated according to error, and adjusts weight and the biasing of each layer.
3. the target locating set of industry sorting machine people as claimed in claim 2, it is characterized in that：K=2.
4. the target locating set of industry sorting machine people as claimed in claim 2, it is characterized in that：In pre-training, use
It is multiple dimensioned to be trained, change the size of input picture every 10 wheels.
5. the target locating set of industry sorting machine people as claimed in claim 4, it is characterized in that：In pre-training, with 32
As the down-sampling factor, it is maintained at the size for inputting picture between 320 to 608.
6. the target locating set of the industrial sorting machine people as described in one of claim 1 ~ 5, it is characterized in that：The convolution mind
Through the improvement that network is on the basis of Darkne-19, the last one convolutional layer is eliminated, increasing 3 convolution kernel sizes is
3x3, the convolutional layer that the convolutional layer and 1 convolution kernel size that port number is 1024 are 1x1.
7. a kind of object localization method of industry sorting machine people, it is characterized in that including the following steps：
The pictorial information of object to be measured is obtained by camera；
Load convolutional neural networks frame and its training pattern；
Using the anchor boxes of several candidates, bounding box is generated in characteristic pattern；
Predict width, height and the centre coordinate and confidence score of bounding box；
The maximum bounding box of confidence score is obtained by non-maxima suppression, corresponding centre coordinate is the position of target.
8. the object localization method of industry sorting machine people as claimed in claim 7, it is characterized in that：The load convolutional Neural
The training pattern of network is the steps of：
The pre-training that network is carried out on ImageNet data set, obtains the pre-training model of network；
Then the data set that target object is obtained by camera, is manually marked, and label data collection is obtained；
To the width and high pass k-means progress clustering of the target frame that label data is concentrated, obtain several groups of candidates'
anchor boxes；
The network output that each layer is obtained by propagated forward, obtains the error of output with label；
The gradient of each layer weight and biasing is successively calculated according to error back propagation, and adjusts weight and the biasing of each layer.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810496379.8A CN108876765A (en) | 2018-05-22 | 2018-05-22 | The target locating set and method of industrial sorting machine people |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810496379.8A CN108876765A (en) | 2018-05-22 | 2018-05-22 | The target locating set and method of industrial sorting machine people |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108876765A true CN108876765A (en) | 2018-11-23 |
Family
ID=64334584
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810496379.8A Pending CN108876765A (en) | 2018-05-22 | 2018-05-22 | The target locating set and method of industrial sorting machine people |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108876765A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110008953A (en) * | 2019-03-29 | 2019-07-12 | 华南理工大学 | Potential target Area generation method based on the fusion of convolutional neural networks multilayer feature |
| CN110135486A (en) * | 2019-05-08 | 2019-08-16 | 西安电子科技大学 | Chopsticks image classification method based on adaptive convolutional neural networks |
| CN110147325A (en) * | 2019-05-22 | 2019-08-20 | 电信科学技术第十研究所有限公司 | A kind of data creation method and device based on automatic test |
| CN110163841A (en) * | 2019-04-12 | 2019-08-23 | 中科院微电子研究所昆山分所 | Detection method, device, equipment and the storage medium of body surface defect |
| CN111401215A (en) * | 2020-03-12 | 2020-07-10 | 杭州涂鸦信息技术有限公司 | Method and system for detecting multi-class targets |
| CN111597857A (en) * | 2019-02-20 | 2020-08-28 | 中科院微电子研究所昆山分所 | Logistics package detection method, device and equipment and readable storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106022237A (en) * | 2016-05-13 | 2016-10-12 | 电子科技大学 | Pedestrian detection method based on end-to-end convolutional neural network |
| CN107358149A (en) * | 2017-05-27 | 2017-11-17 | 深圳市深网视界科技有限公司 | A kind of human body attitude detection method and device |
| CN107862694A (en) * | 2017-12-19 | 2018-03-30 | 济南大象信息技术有限公司 | A kind of hand-foot-and-mouth disease detecting system based on deep learning |
-
2018
- 2018-05-22 CN CN201810496379.8A patent/CN108876765A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106022237A (en) * | 2016-05-13 | 2016-10-12 | 电子科技大学 | Pedestrian detection method based on end-to-end convolutional neural network |
| CN107358149A (en) * | 2017-05-27 | 2017-11-17 | 深圳市深网视界科技有限公司 | A kind of human body attitude detection method and device |
| CN107862694A (en) * | 2017-12-19 | 2018-03-30 | 济南大象信息技术有限公司 | A kind of hand-foot-and-mouth disease detecting system based on deep learning |
Non-Patent Citations (1)
| Title |
|---|
| 魏湧明 等: "基于YOLOv2的无人机航拍图像定位研究", 《激光与光电子学进展》 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111597857A (en) * | 2019-02-20 | 2020-08-28 | 中科院微电子研究所昆山分所 | Logistics package detection method, device and equipment and readable storage medium |
| CN111597857B (en) * | 2019-02-20 | 2023-09-26 | 中科微至科技股份有限公司 | Logistics package detection method, device, equipment and readable storage medium |
| CN110008953A (en) * | 2019-03-29 | 2019-07-12 | 华南理工大学 | Potential target Area generation method based on the fusion of convolutional neural networks multilayer feature |
| CN110008953B (en) * | 2019-03-29 | 2023-04-28 | 华南理工大学 | Potential target area generation method based on convolution neural network multi-layer feature fusion |
| CN110163841A (en) * | 2019-04-12 | 2019-08-23 | 中科院微电子研究所昆山分所 | Detection method, device, equipment and the storage medium of body surface defect |
| CN110163841B (en) * | 2019-04-12 | 2021-05-14 | 中科微至智能制造科技江苏股份有限公司 | Method, device and equipment for detecting surface defects of object and storage medium |
| CN110135486A (en) * | 2019-05-08 | 2019-08-16 | 西安电子科技大学 | Chopsticks image classification method based on adaptive convolutional neural networks |
| CN110135486B (en) * | 2019-05-08 | 2023-01-24 | 西安电子科技大学 | Chopstick image classification method based on adaptive convolutional neural network |
| CN110147325A (en) * | 2019-05-22 | 2019-08-20 | 电信科学技术第十研究所有限公司 | A kind of data creation method and device based on automatic test |
| CN110147325B (en) * | 2019-05-22 | 2023-04-07 | 电信科学技术第十研究所有限公司 | Data generation method and device based on automatic test |
| CN111401215A (en) * | 2020-03-12 | 2020-07-10 | 杭州涂鸦信息技术有限公司 | Method and system for detecting multi-class targets |
| CN111401215B (en) * | 2020-03-12 | 2023-10-31 | 杭州涂鸦信息技术有限公司 | Multi-class target detection method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108846415A (en) | The Target Identification Unit and method of industrial sorting machine people | |
| CN108876765A (en) | The target locating set and method of industrial sorting machine people | |
| CN111062915B (en) | Real-time steel pipe defect detection method based on improved YOLOv3 model | |
| Yu et al. | A vision-based robotic grasping system using deep learning for 3D object recognition and pose estimation | |
| Tu et al. | An accurate and real-time surface defects detection method for sawn lumber | |
| CN110314854A (en) | A kind of device and method of the workpiece sensing sorting of view-based access control model robot | |
| CN110660052A (en) | Hot-rolled strip steel surface defect detection method based on deep learning | |
| CN107316058A (en) | Improve the method for target detection performance by improving target classification and positional accuracy | |
| CN107451602A (en) | A kind of fruits and vegetables detection method based on deep learning | |
| Wu et al. | Solder joint recognition using mask R-CNN method | |
| CN110633738B (en) | Rapid classification method for industrial part images | |
| CN110610210B (en) | Multi-target detection method | |
| CN110490842A (en) | A kind of steel strip surface defect detection method based on deep learning | |
| CN114581782B (en) | Fine defect detection method based on coarse-to-fine detection strategy | |
| CN110334594A (en) | A kind of object detection method based on batch again YOLO algorithm of standardization processing | |
| CN111275684A (en) | Strip steel surface defect detection method based on multi-scale feature extraction | |
| CN111091534A (en) | Target detection-based pcb defect detection and positioning method | |
| Li et al. | Autofeeding system for assembling the CBCs on automobile engine based on 3-D vision guidance | |
| Raj et al. | Steel strip quality assurance with yolov7-csf: a coordinate attention and siou fusion approach | |
| Mouzenidis et al. | Multi-modal variational faster r-cnn for improved visual object detection in manufacturing | |
| Chen et al. | Detection of Polarizer Surface Defects Based on an Improved Lightweight YOLOv3 Model | |
| Mei et al. | Target recognition and grabbing positioning method based on convolutional neural network | |
| Wu et al. | Spoon surface defect detection based on improved yolo v3 | |
| Bakalets et al. | Using neural network to ensure the packing quality of pasteurized milk | |
| Qi et al. | Fastener counting method with an improved Blendmask |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20181123 |
|
| RJ01 | Rejection of invention patent application after publication |