CN105181708A - Qualification detection platform of sewing needles in batches - Google Patents
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Abstract
The invention relates to a qualification detection platform of sewing needles in batches. The platform comprises a sewing needle conveying mechanism, a piece of image acquisition equipment, a piece of image processing equipment and a piece of master control equipment; the sewing needle conveying mechanism is used for conveying all sewing needles of a batch in order; the image processing equipment acquires a sewing needle image corresponding to each sewing needle on the sewing needle conveying mechanism; the image processing equipment is connected with the master control equipment for image processing, in order to determine whether the sewing needle corresponding to the sewing needle image is a defective sewing needle; the master control equipment and the image processing equipment are connected, whether the detected sewing needles batch are qualified is determined based on the image processing result of the image processing equipment. According to the invention, defects of the sewing needle to be detected can be accurately identified, thereby improving examination precision of sewing needles in batches.
Description
Technical field
The present invention relates to eedle lot inspection field, particularly relate to a kind of weaving eedle batch qualified detection platform.
Background technology
Eedle is because quantity is many, shape is little and be difficult to detect in batch, at present to the detection of eedle in batch mainly from outward appearance, first threshold value is selected by eedle from background separation, then the eedle separated whether existing defects is judged, after being marked by the eedle of existing defects, manual type is adopted to be taken out by the eedle of marking of defects.
But, there is the problem cannot determining optimal segmenting threshold in eedle of the prior art batch detection scheme, eedle is caused to be split clean not, the defects detection that remote effect are follow-up, lack effective batch of qualified identification mechanism with Image semantic classification is machine-processed targetedly simultaneously, more crucially, the artificial mode too backwardness rejecting defect eedle, affects the eedle batch efficiency detected.
For this reason, the present invention proposes a kind of eedle batch qualified detection platform, the separating effect of eedle and background can be improved, and the mode of machinery rejecting can be adopted to substitute the artificial mode rejected, thus ensure the speed of eedle detection in batch while improving the accuracy that eedle detects in batch.
Summary of the invention
In order to solve the technical matters that prior art exists, the invention provides a kind of eedle batch qualified detection platform, according to the external appearance characteristic of eedle, utilize medium filtering subset, contrast strengthen subset, gray processing process subset, Threshold selection subset, Target Segmentation subset and defects detection subset these targetedly image processing equipment defects detection is carried out to the outward appearance of each eedle in eedle in batch, set up a batch qualified identification mechanism simultaneously, carry out perfect to an eedle batch qualified detection scheme on the whole.
According to an aspect of the present invention, provide a kind of eedle batch qualified detection platform, described detection platform arranges and comprises eedle connecting gear, image capture device, image processing equipment and main control equipment, described eedle connecting gear is used for all eedles transmitting batch successively, described image capture device is to the corresponding eedle image of each eedle collection on eedle connecting gear, described image processing equipment is connected with described image capture device, image procossing is carried out to described eedle image, to determine whether the eedle that described eedle image is corresponding is defect eedle, described main control equipment is connected with described image processing equipment, processing result image based on described image processing equipment determines whether the eedle detected is batch qualified.
More specifically, in described eedle batch qualified detection platform, also comprise: portable hard drive, for prestoring eedle gray threshold scope, all defect gray threshold in described eedle gray threshold scope all value between 0-255, described portable hard drive is also for prestoring default defect threshold value and predetermined number threshold value, described portable hard drive also for prestoring the datum target image that prestores, described in the datum target image that prestores be that the image only only having norm force machine pin pixel obtained is taken to benchmark eedle, power-supply unit, comprise solar powered device, commercial power interface, change-over switch and electric pressure converter, described change-over switch is connected respectively with described solar powered device and described commercial power interface, line voltage size according to commercial power interface place determines whether be switched to described solar powered device to be powered by described solar powered device, described electric pressure converter is connected with described change-over switch, with the 5V voltage transitions will inputted by change-over switch for 3.3V voltage, described image capture device is color camera, and it is positioned at directly over the middle part of described eedle connecting gear, and the resolution of its eedle image gathered is 3840 × 2160, described image processing equipment is connected respectively with described image capture device and described portable hard drive, for receiving described eedle image, described image processing equipment comprises medium filtering subset, contrast strengthen subset, gray processing process subset, Threshold selection subset, Target Segmentation subset and defects detection subset, described medium filtering subset is connected with described image capture device, for performing the medium filtering process of 5 × 5 pixel filter windows to described visible images, to obtain filtering image, described contrast strengthen subset is connected with described medium filtering subset, for performing contrast enhancement processing, to obtain enhancing image to filtering image, described gray processing process subset is connected with described contrast strengthen subset, for performing gray processing process, to obtain gray level image to described enhancing image, described Threshold selection subset is connected respectively with described portable hard drive and described gray processing process subset, for selecting a value as preliminary election gray threshold successively from eedle gray threshold scope, preliminary election gray threshold is adopted gray level image to be divided into preliminary election background area and pre-selected target region, calculate preliminary election background area and occupy the area ratio of gray level image as the first area ratio, calculate the pixel average gray value of preliminary election background area as the first average gray value, calculate pre-selected target region and occupy the area ratio of gray level image as second area ratio, calculate the pixel average gray value in pre-selected target region as the second average gray value, first average gray value is deducted the overall average gray-scale value of gray level image, the difference obtained square be multiplied by the first area ratio to obtain the first product, second average gray value is deducted the overall average gray-scale value of gray level image, the difference obtained square to be multiplied by second area than to obtain the second product, by the first sum of products second product addition to obtain and value, select and be worth maximum preliminary election gray threshold as target gray threshold value, described Target Segmentation subset is connected with described Threshold selection subset, for adopting target gray threshold value, gray level image is divided into background image and target image, described defects detection subset is connected with described Target Segmentation subset, the gray-scale value summation calculating all pixels in target image is using as the first gray-scale value summation, the gray-scale value summation calculating all pixels in datum target image of prestoring is using as the second gray-scale value summation, first gray-scale value summation is deducted the absolute value of the difference that the second gray-scale value summation obtains as defect reference value, when defect reference value is greater than default defect threshold value, judge target existing defects and export existing defects signal, when defect reference value is less than or equal to default defect threshold value, judge target not existing defects export not existing defects signal, described eedle connecting gear, comprises servomotor, eedle travelling belt and multiple rotation roller bearing, and multiple rotation roller bearing drives the eedle of eedle travelling belt horizontal transmission above it, and servomotor is for driving multiple rotation roller bearing, described main control equipment is built-in with counter, described counter is connected with described image processing equipment, when receiving the existing defects signal that described image processing equipment sends, the count value of counter adds 1, described main control equipment is when the count value of described counter is more than or equal to described predetermined number threshold value, send a batch defective signal, when the count value of described counter is less than described predetermined number threshold value, send a batch qualifying signal, sound and light alarm equipment, is connected with described main control equipment, for when receiving batch defective signal that described main control equipment sends, carries out corresponding sound and light alarm operation, wherein, described medium filtering subset, described contrast strengthen subset, described gray processing process subset, described Threshold selection subset, described Target Segmentation subset and described defects detection subset are integrated in same fpga chip, high-speed cache dual port RAM is provided with between described fpga chip and described main control equipment.
More specifically, in described eedle batch qualified detection platform, described detection platform also comprises: EPLD control circuit, connect described high-speed cache dual port RAM, described fpga chip and described main control equipment, for controlling described high-speed cache dual port RAM, data interaction between described fpga chip and described main control equipment and sequential.
More specifically, in described eedle batch qualified detection platform: described color camera also comprises light source utility appliance, for providing floor light for the image acquisition of described color camera.
More specifically, in described eedle batch qualified detection platform: described color camera also comprises luminance sensor, for detecting the brightness of surrounding environment.
More specifically, in described eedle batch qualified detection platform: described light source utility appliance is connected with described luminance sensor, the light intensity of the brilliance control floor light of the surrounding environment detected based on described luminance sensor.
Accompanying drawing explanation
Below with reference to accompanying drawing, embodiment of the present invention are described, wherein:
Fig. 1 is the block diagram of the eedle batch qualified detection platform illustrated according to an embodiment of the present invention.
Reference numeral: 1 eedle connecting gear; 2 image capture devices; 3 image processing equipments; 4 main control equipments
Embodiment
Below with reference to accompanying drawings the embodiment of eedle of the present invention batch qualified detection platform is described in detail.
Eedle is the sewing kit that sewing field is commonly used, and its quality determines the quality of sewing efficiency.Because eedle outward appearance is tiny, detection mode in batch is generally adopted to carry out quality analysis.But an eedle batch qualified detection scheme exists eedle segmentation threshold and is difficult to select, lack analysis mechanisms in batch in prior art, have a strong impact on speed and efficiency that finished product eedle analyzes.
In order to overcome above-mentioned deficiency, the present invention has built a kind of eedle batch qualified detection platform, adopts the selected mode of adaptivenon-uniform sampling threshold value by the eedle in detected image and background separation, introduces analysis mechanisms in batch simultaneously, thus solve the problems of the technologies described above.
Fig. 1 is the block diagram of the eedle batch qualified detection platform illustrated according to an embodiment of the present invention, described detection platform comprises eedle connecting gear, image capture device, image processing equipment and main control equipment, described eedle connecting gear is used for all eedles transmitting batch successively, described image capture device is to the corresponding eedle image of each eedle collection on eedle connecting gear, described image processing equipment is connected with described image capture device, image procossing is carried out to described eedle image, to determine whether the eedle that described eedle image is corresponding is defect eedle, described main control equipment is connected with described image processing equipment, processing result image based on described image processing equipment determines whether the eedle detected is batch qualified.
Then, continue to be further detailed the concrete structure of eedle of the present invention batch qualified detection platform.
Described detection platform also comprises: portable hard drive, for prestoring eedle gray threshold scope, all defect gray threshold in described eedle gray threshold scope all value between 0-255; Described portable hard drive is also for prestoring default defect threshold value and predetermined number threshold value; Described portable hard drive also for prestoring the datum target image that prestores, described in the datum target image that prestores be that the image only only having norm force machine pin pixel obtained is taken to benchmark eedle.
Described detection platform also comprises: power-supply unit, comprise solar powered device, commercial power interface, change-over switch and electric pressure converter, described change-over switch is connected respectively with described solar powered device and described commercial power interface, line voltage size according to commercial power interface place determines whether be switched to described solar powered device to be powered by described solar powered device, described electric pressure converter is connected with described change-over switch, with the 5V voltage transitions will inputted by change-over switch for 3.3V voltage.
Described image capture device is color camera, and it is positioned at directly over the middle part of described eedle connecting gear, and the resolution of its eedle image gathered is 3840 × 2160.
Described image processing equipment is connected respectively with described image capture device and described portable hard drive, for receiving described eedle image.
Described image processing equipment comprises medium filtering subset, contrast strengthen subset, gray processing process subset, Threshold selection subset, Target Segmentation subset and defects detection subset.
Described medium filtering subset is connected with described image capture device, for performing the medium filtering process of 5 × 5 pixel filter windows to described visible images, to obtain filtering image; Described contrast strengthen subset is connected with described medium filtering subset, for performing contrast enhancement processing, to obtain enhancing image to filtering image; Described gray processing process subset is connected with described contrast strengthen subset, for performing gray processing process, to obtain gray level image to described enhancing image.
Described Threshold selection subset is connected respectively with described portable hard drive and described gray processing process subset, for selecting a value as preliminary election gray threshold successively from eedle gray threshold scope, preliminary election gray threshold is adopted gray level image to be divided into preliminary election background area and pre-selected target region, calculate preliminary election background area and occupy the area ratio of gray level image as the first area ratio, calculate the pixel average gray value of preliminary election background area as the first average gray value, calculate pre-selected target region and occupy the area ratio of gray level image as second area ratio, calculate the pixel average gray value in pre-selected target region as the second average gray value, first average gray value is deducted the overall average gray-scale value of gray level image, the difference obtained square be multiplied by the first area ratio to obtain the first product, second average gray value is deducted the overall average gray-scale value of gray level image, the difference obtained square to be multiplied by second area than to obtain the second product, by the first sum of products second product addition to obtain and value, select and be worth maximum preliminary election gray threshold as target gray threshold value.
Described Target Segmentation subset is connected with described Threshold selection subset, for adopting target gray threshold value, gray level image is divided into background image and target image.
Described defects detection subset is connected with described Target Segmentation subset, the gray-scale value summation calculating all pixels in target image is using as the first gray-scale value summation, the gray-scale value summation calculating all pixels in datum target image of prestoring is using as the second gray-scale value summation, first gray-scale value summation is deducted the absolute value of the difference that the second gray-scale value summation obtains as defect reference value, when defect reference value is greater than default defect threshold value, judge target existing defects and export existing defects signal, when defect reference value is less than or equal to default defect threshold value, judge target not existing defects export not existing defects signal.
Described eedle connecting gear comprises servomotor, eedle travelling belt and multiple rotation roller bearing, and multiple rotation roller bearing drives the eedle of eedle travelling belt horizontal transmission above it, and servomotor is for driving multiple rotation roller bearing; Described main control equipment is built-in with counter, described counter is connected with described image processing equipment, when receiving the existing defects signal that described image processing equipment sends, the count value of counter adds 1, described main control equipment is when the count value of described counter is more than or equal to described predetermined number threshold value, send a batch defective signal, when the count value of described counter is less than described predetermined number threshold value, send a batch qualifying signal.
Described detection platform also comprises: sound and light alarm equipment, is connected with described main control equipment, for when receiving batch defective signal that described main control equipment sends, carries out corresponding sound and light alarm operation.
Wherein, described medium filtering subset, described contrast strengthen subset, described gray processing process subset, described Threshold selection subset, described Target Segmentation subset and described defects detection subset are integrated in same fpga chip; High-speed cache dual port RAM is provided with between described fpga chip and described main control equipment.
Alternatively, in described detection platform: described detection platform also comprises: EPLD control circuit, connect described high-speed cache dual port RAM, described fpga chip and described main control equipment, for controlling described high-speed cache dual port RAM, data interaction between described fpga chip and described main control equipment and sequential; Described color camera also comprises light source utility appliance, for providing floor light for the image acquisition of described color camera; Described color camera also comprises luminance sensor, for detecting the brightness of surrounding environment; And described light source utility appliance can also be connected with described luminance sensor, the light intensity of the brilliance control floor light of the surrounding environment detected based on described luminance sensor.
In addition, FPGA (Field-ProgrammableGateArray), i.e. field programmable gate array, he is the product further developed on the basis of the programming devices such as PAL, GAL, CPLD.He occurs as a kind of semi-custom circuit in special IC (ASIC) field, has both solved the deficiency of custom circuit, overcomes again the shortcoming that original programming device gate circuit number is limited.
With the circuit design that hardware description language (Verilog or VHDL) completes, can through simple comprehensive and layout, being burned onto fast on FPGA and testing, is the technology main flow of modern IC designs checking.These can be edited element and can be used to realize some basic logic gates (such as AND, OR, XOR, NOT) or more more complex combination function such as demoder or mathematical equation.Inside most FPGA, in these editable elements, also comprise memory cell such as trigger (Flip-flop) or other more complete block of memory.System designer can be coupled together the logical block of FPGA inside by editable connection as required, just looks like that a breadboard has been placed in a chip.One dispatch from the factory after the logical block of finished product FPGA can change according to deviser with being connected, so FPGA can complete required logic function.
FPGA is in general slow than the speed of ASIC (special IC), realizes same function ratio ASIC circuit area and wants large.But they also have a lot of advantages such as can finished product fast, can be modified the mistake in correction program and more cheap cost.Manufacturer also may provide the FPGA of cheap still edit capability difference.Because these chips have poor can edit capability, so exploitations of these designs complete on common FPGA, then design is transferred to one and is similar on the chip of ASIC.Another method is with CPLD (ComplexProgrammableLogicDevice, CPLD).The exploitation of FPGA has a great difference relative to the exploitation of conventional P C, single-chip microcomputer.FPGA, based on concurrent operation, realizes with hardware description language; Very large difference is had compared to the sequential operation of PC or single-chip microcomputer (no matter being von Neumann structure or Harvard structure).
As far back as 1980 mid-nineties 90s, FPGA takes root in PLD equipment.CPLD and FPGA includes the Programmadle logic unit of some relatively large amount.The density of CPLD logic gate is between several thousand to several ten thousand logical blocks, and FPGA normally arrives millions of several ten thousand.The key distinction of CPLD and FPGA is their system architecture.CPLD is a somewhat restrictive structure.This structure is arranged by the logical groups of one or more editable result sum and forms with the register of the locking of some relatively small amounts.Such result lacks editor's dirigibility, but but have the time delay and logical block that can estimate to the advantage of linkage unit height ratio.And FPGA has a lot of linkage units, although allow him edit more flexibly like this, structure is complicated many.
Adopt eedle of the present invention batch qualified detection platform, for the technical matters of batch qualified detection difficult of eedle in prior art, for the appearance characteristics of eedle, introduce adaptivenon-uniform sampling threshold determination mechanism and error image defect location mechanism, ensure the accuracy of eedle defects detection, by the qualified identification of eedle is machine-processed in batch, eedle batch is effectively assert simultaneously, while the speed improving eedle batch quality identification, ensured the accuracy assert.
Be understandable that, although the present invention with preferred embodiment disclose as above, but above-described embodiment and be not used to limit the present invention.For any those of ordinary skill in the art, do not departing under technical solution of the present invention ambit, the technology contents of above-mentioned announcement all can be utilized to make many possible variations and modification to technical solution of the present invention, or be revised as the Equivalent embodiments of equivalent variations.Therefore, every content not departing from technical solution of the present invention, according to technical spirit of the present invention to any simple modification made for any of the above embodiments, equivalent variations and modification, all still belongs in the scope of technical solution of the present invention protection.
Claims (6)
1. an eedle batch qualified detection platform, described detection platform comprises eedle connecting gear, image capture device, image processing equipment and main control equipment, described eedle connecting gear is used for all eedles transmitting batch successively, described image capture device is to the corresponding eedle image of each eedle collection on eedle connecting gear, described image processing equipment is connected with described image capture device, image procossing is carried out to described eedle image, to determine whether the eedle that described eedle image is corresponding is defect eedle, described main control equipment is connected with described image processing equipment, processing result image based on described image processing equipment determines whether the eedle detected is batch qualified.
2. eedle batch qualified detection platform as claimed in claim 1, it is characterized in that, described detection platform also comprises:
Portable hard drive, for prestoring eedle gray threshold scope, all defect gray threshold in described eedle gray threshold scope all value between 0-255; Described portable hard drive is also for prestoring default defect threshold value and predetermined number threshold value; Described portable hard drive also for prestoring the datum target image that prestores, described in the datum target image that prestores be that the image only only having norm force machine pin pixel obtained is taken to benchmark eedle;
Power-supply unit, comprise solar powered device, commercial power interface, change-over switch and electric pressure converter, described change-over switch is connected respectively with described solar powered device and described commercial power interface, line voltage size according to commercial power interface place determines whether be switched to described solar powered device to be powered by described solar powered device, described electric pressure converter is connected with described change-over switch, with the 5V voltage transitions will inputted by change-over switch for 3.3V voltage;
Described image capture device is color camera, and it is positioned at directly over the middle part of described eedle connecting gear, and the resolution of its eedle image gathered is 3840 × 2160;
Described image processing equipment is connected respectively with described image capture device and described portable hard drive, for receiving described eedle image, described image processing equipment comprises medium filtering subset, contrast strengthen subset, gray processing process subset, Threshold selection subset, Target Segmentation subset and defects detection subset, described medium filtering subset is connected with described image capture device, for performing the medium filtering process of 5 × 5 pixel filter windows to described visible images, to obtain filtering image, described contrast strengthen subset is connected with described medium filtering subset, for performing contrast enhancement processing, to obtain enhancing image to filtering image, described gray processing process subset is connected with described contrast strengthen subset, for performing gray processing process, to obtain gray level image to described enhancing image, described Threshold selection subset is connected respectively with described portable hard drive and described gray processing process subset, for selecting a value as preliminary election gray threshold successively from eedle gray threshold scope, preliminary election gray threshold is adopted gray level image to be divided into preliminary election background area and pre-selected target region, calculate preliminary election background area and occupy the area ratio of gray level image as the first area ratio, calculate the pixel average gray value of preliminary election background area as the first average gray value, calculate pre-selected target region and occupy the area ratio of gray level image as second area ratio, calculate the pixel average gray value in pre-selected target region as the second average gray value, first average gray value is deducted the overall average gray-scale value of gray level image, the difference obtained square be multiplied by the first area ratio to obtain the first product, second average gray value is deducted the overall average gray-scale value of gray level image, the difference obtained square to be multiplied by second area than to obtain the second product, by the first sum of products second product addition to obtain and value, select and be worth maximum preliminary election gray threshold as target gray threshold value, described Target Segmentation subset is connected with described Threshold selection subset, for adopting target gray threshold value, gray level image is divided into background image and target image, described defects detection subset is connected with described Target Segmentation subset, the gray-scale value summation calculating all pixels in target image is using as the first gray-scale value summation, the gray-scale value summation calculating all pixels in datum target image of prestoring is using as the second gray-scale value summation, first gray-scale value summation is deducted the absolute value of the difference that the second gray-scale value summation obtains as defect reference value, when defect reference value is greater than default defect threshold value, judge target existing defects and export existing defects signal, when defect reference value is less than or equal to default defect threshold value, judge target not existing defects export not existing defects signal,
Described eedle connecting gear, comprises servomotor, eedle travelling belt and multiple rotation roller bearing, and multiple rotation roller bearing drives the eedle of eedle travelling belt horizontal transmission above it, and servomotor is for driving multiple rotation roller bearing;
Described main control equipment is built-in with counter, described counter is connected with described image processing equipment, when receiving the existing defects signal that described image processing equipment sends, the count value of counter adds 1, described main control equipment is when the count value of described counter is more than or equal to described predetermined number threshold value, send a batch defective signal, when the count value of described counter is less than described predetermined number threshold value, send a batch qualifying signal;
Sound and light alarm equipment, is connected with described main control equipment, for when receiving batch defective signal that described main control equipment sends, carries out corresponding sound and light alarm operation;
Wherein, described medium filtering subset, described contrast strengthen subset, described gray processing process subset, described Threshold selection subset, described Target Segmentation subset and described defects detection subset are integrated in same fpga chip;
Wherein, between described fpga chip and described main control equipment, high-speed cache dual port RAM is provided with.
3. eedle batch qualified detection platform as claimed in claim 2, it is characterized in that, described detection platform also comprises:
EPLD control circuit, connects described high-speed cache dual port RAM, described fpga chip and described main control equipment, for controlling described high-speed cache dual port RAM, data interaction between described fpga chip and described main control equipment and sequential.
4. eedle batch qualified detection platform as claimed in claim 3, is characterized in that:
Described color camera also comprises light source utility appliance, for providing floor light for the image acquisition of described color camera.
5. eedle batch qualified detection platform as claimed in claim 4, is characterized in that:
Described color camera also comprises luminance sensor, for detecting the brightness of surrounding environment.
6. eedle batch qualified detection platform as claimed in claim 5, is characterized in that:
Described light source utility appliance is connected with described luminance sensor, the light intensity of the brilliance control floor light of the surrounding environment detected based on described luminance sensor.
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| CN111402190A (en) * | 2018-12-28 | 2020-07-10 | 上海尤图智能科技有限公司 | Image processing device and system for wire rod on-line detection |
| CN112586539A (en) * | 2020-11-08 | 2021-04-02 | 泰州市华仕达机械制造有限公司 | Self-adaptive belt return control platform |
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| CN111402190B (en) * | 2018-12-28 | 2023-06-09 | 上海尤图智能科技有限公司 | Image processing device and system for wire rod on-line detection |
| CN112586539A (en) * | 2020-11-08 | 2021-04-02 | 泰州市华仕达机械制造有限公司 | Self-adaptive belt return control platform |
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