JP7301782B2 - Composition analysis method for electronic/electrical equipment parts scrap, electronic/electrical equipment parts scrap processing method, electronic/electrical equipment parts scrap composition analysis device, and electronic/electrical equipment parts scrap processing equipment - Google Patents

Description

The present invention relates to a method for analyzing the composition of electronic/electrical equipment parts scrap, a method for processing electronic/electrical equipment parts scrap, a composition analysis apparatus for electronic/electrical equipment parts scrap, and an electronic/electrical equipment parts scrap processing apparatus.

In recent years, from the viewpoint of resource conservation, it has become increasingly popular to recover valuable metals from electronic and electrical device parts scraps such as waste home appliances, PCs, mobile phones, and the like. In recent years, the amount of electronic and electrical equipment parts scraps to be processed has been increasing, and efficient collection methods have been studied and proposed.

For example, in Japanese Patent Application Laid-Open No. 2015-123418 (Patent Document 1), after incinerating electrical and electronic device component scraps containing copper, they are pulverized to a predetermined size or less, and the pulverized electrical and electronic device component scraps are smelted with copper. Furnace processing is described.

However, due to the increase in the processing amount of electronic and electrical equipment parts scraps, some of the substances contained in the electronic and electrical equipment parts scraps are unfavorable substances (smelting inhibitors) for the subsequent copper smelting process. It will be put in more than before. If the amount of smelting inhibitors charged into such a copper smelting process increases, there arises a situation where the amount of electronic/electrical equipment parts scraps charged must be limited.

For example, electronic and electrical equipment parts scraps include parts scraps of various shapes and types, and the raw material composition varies depending on the difference of the supplier. In order to properly sort out the raw materials that are put into the copper smelting process, currently, the raw material composition of electronic and electrical equipment parts scraps is evaluated in advance by visual judgment and chemical analysis, and the results are used in the operation management and operating conditions of the sorting process. are reflected in the settings of

However, in the method of visually determining the raw material composition, evaluation results vary depending on individual experience and skills, and quantitative evaluation is not possible. Chemical analysis and manual sorting to identify raw material composition also take time.

JP 2015-123418 A

The present disclosure provides a composition analysis method for electronic/electrical device parts scraps that enables efficient analysis of the composition of electronic/electrical device parts scraps in a short period of time, regardless of individual experience and skills. Provided are a method for processing electronic/electrical device parts scraps, a composition analysis apparatus for electronic/electrical device parts scraps, and a processing apparatus for electronic/electrical device parts scraps.

In one embodiment of the method for analyzing the composition of electronic/electrical device parts scrap according to the embodiment of the present invention, a plurality of parts are selected from captured images obtained by imaging a plurality of electronic/electrical device parts scraps including a plurality of component types. Extract electronic/electrical equipment parts scrap for each product type, and assign a recognition frame including the electronic/electrical equipment parts scrap and the background image around the electronic/electrical equipment parts scrap to the extracted electronic/electrical equipment parts scrap. Then, based on the part type area ratio data having at least the information of the area ratio of the electronic/electrical equipment parts scrap to the recognition frame, the total area of the electronic/electrical equipment parts scrap attached with the recognition frame for each of the multiple part types. , multiplies the estimated total area by the assumed weight per unit area for each of the multiple types of parts, and analyzes the weight ratio of electronic and electrical equipment parts scrap for each of the multiple types of parts. A method for analyzing the composition of electronic/electrical device scrap in an image includes analyzing the composition of the electronic/electrical device scrap.

In one embodiment of the method for processing electronic/electrical device component scraps according to the embodiment of the present invention, a specific component type is selected from a plurality of component types based on the analysis result of the composition of the electronic/electrical device component waste. It is a method for processing electronic and electrical equipment parts scrap including a sorting step of sorting out.

In one embodiment of the electronic/electrical device component scrap composition analysis apparatus according to the embodiment of the present invention, an electronic/electrical device component is selected from captured images obtained by imaging a plurality of electronic/electrical device component scraps including a plurality of component types. Extracting means for extracting parts scrap; Recognition frame providing means for attaching a recognition frame including an electronic/electrical equipment parts scrap and a background image around the electronic/electrical equipment parts scrap to the extracted electronic/electrical equipment parts scrap. And, based on the part type area ratio data having the information of the area ratio of the electronic / electrical equipment parts scrap to the recognition frame, the total area of the electronic / electrical equipment parts scrap attached with the recognition frame is calculated for each of the multiple part types. Multiplying the total area estimation result by the estimated weight per unit area for each of a plurality of types of parts by means of estimating area to be estimated, and analyzing the weight ratio of electronic and electrical equipment parts scrap for each of a plurality of types of parts. and an analysis means for analyzing the composition of the electronic/electrical equipment parts scrap in the captured image.

In one embodiment of the processing apparatus for electronic/electrical device parts scraps according to the embodiment of the present invention, an imaging means for imaging a plurality of electronic/electrical device parts scraps including a plurality of types of parts;・Extraction means for extracting electrical equipment parts scrap, and a recognition frame for adding a recognition frame including an electronic/electrical equipment parts scrap and a background image around the electronic/electrical equipment parts scrap to the extracted electronic/electrical equipment parts scrap Giving means, total area of electronic/electrical device parts scrap to which a recognition frame is attached for each of a plurality of component types, based on part type area ratio data having information on the area ratio of electronic/electrical device parts scrap to the recognition frame and an area estimation means for estimating the total area and the estimated weight per unit area for each of a plurality of types of parts, and analyzing the weight ratio of electronic and electrical equipment parts scrap for each of a plurality of types of parts. By doing so, a composition analysis device equipped with an analysis means for analyzing the composition of the electronic / electrical equipment parts scrap in the captured image, and a specific from the electronic / electrical equipment parts scrap based on the composition analysis result analyzed by the composition analysis equipment A processing apparatus for scrap electronic/electrical equipment parts is provided, which includes a sorter for sorting scrap parts.

According to the present disclosure, a composition analysis method for electronic/electrical device parts scrap that enables efficient analysis of the composition of electronic/electrical device parts scrap in a short time regardless of individual experience and skills, It is possible to provide a method for processing electronic/electrical device parts scrap, a composition analysis apparatus for electronic/electrical device parts scrap, and a processing apparatus for electronic/electrical device parts scrap.

BRIEF DESCRIPTION OF THE DRAWINGS It is a block diagram which shows the processing apparatus of the electronic/electrical equipment components waste which concerns on embodiment of this invention. FIG. 2(a) is a photograph showing an example of an image in which a recognition frame is given to an electronic/electrical device component waste (substrate) present in a captured image, and FIG. 2(b) is an electronic・It is a photograph showing an example of arranging electric equipment parts scraps for each part type (substrate/plastic). It is a flow chart which shows an example of image analysis processing of electronic/electric equipment parts scrap. 4 is a graph showing a comparison between measured values and estimated values of the total area of the substrate and plastic.

BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, embodiments of the present invention will be described with reference to the drawings. The embodiments shown below are examples of devices and methods for embodying the technical idea of the present invention. It is not specific.

As shown in FIG. 1, the electronic/electrical device component scrap processing apparatus according to the embodiment of the present invention includes an imaging device 12 for capturing an image of electronic/electrical device component scrap, and an electronic/electrical device component scrap analyzing device for composition. and a sorter 13 for sorting out specific scrap parts from electronic/electrical equipment scrap parts based on the composition analysis result analyzed by the composition analysis apparatus 10 .

"Electronic/electrical device parts scrap" in the present embodiment refers to scraps obtained by crushing electronic/electrical devices such as waste home appliances, PCs, and mobile phones, and after being collected, crushed to an appropriate size. point to In the present embodiment, the crushing to obtain electronic/electrical device parts scraps may be performed by the processor himself/herself, or the crushed parts purchased in the market may be used.

The crushing method is not limited to a specific device, and may be a shearing method or an impact method. Therefore, it does not include equipment belonging to the category of grinders whose purpose is to grind finely.

Electronic and electrical equipment parts scrap consists of multiple types of parts such as plastics (synthetic resins) used for substrates, housings, etc., metal pieces, copper wire scraps, capacitors, IC chips, and others. can be classified in more detail. Although it is not limited to the following, in the present embodiment, it is possible to suitably process electronic and electrical equipment component scraps that have been crushed to a particle size of 50 mm or less. Although the lower limit of the particle size is not particularly limited, it is 5 mm or more, more typically 10 mm or more, and further 15 mm or more.

The composition analysis apparatus 10 can include a control unit (control device) 100 for controlling composition analysis processing, a storage device 110 for storing information necessary for various controls, an input device 120 and a display device 130 . The control unit 100 can include extraction means 101 , recognition frame provision means 102 , area estimation means 103 , analysis means 104 , operating condition generation means 105 , position information output means 106 , learning means 107 and update means 108 .

The storage device 110 can include extraction data storage means 111 , parts area ratio storage means 112 , analysis information storage means 113 and additional information storage means 114 . The analyzing means 104 can input/output the analysis result of the analyzing means 104 to the sorting machine 14 other than the sorting machine 13 connected through the server 15 or the network 11 via the network 11. ing.

The extraction data storage means 111 stores extraction data for classifying and extracting images of electronic/electrical device component scraps for each of a plurality of component types from images of the electronic/electrical device component scraps. there is For example, the extracted data storage means 111 can extract a plurality of component types from the image information of electronic/electrical equipment component scraps, namely substrates, plastics, metal pieces, copper wire scraps, capacitors, IC chips, and others (connectors, film-like components). scraps, coated wire scraps, etc.) are provided with extraction basic information for classifying them into at least two types, preferably seven types or more, and further, classification into ten types or more. Conditions for classifying and extracting electronic/electrical device parts scrap by a plurality of types of parts can be set in advance by the operator according to various conditions such as the purpose of subsequent sorting processing.

The extraction data used in the extraction process includes, for example, shape information for extracting geometric shapes such as outlines of electronic/electrical equipment parts scraps, color information for extracting colors of electronic/electrical equipment parts scraps, electronic - Background information for extracting background information such as the color of the background image around electrical equipment parts scraps, shadows due to unevenness, etc., and rotating images of electronic/electrical equipment parts scraps can be included. The extracted data are stored in the extracted data storage means 111 . The extracting means 101 extracts electronic/electrical equipment parts scrap for each of a plurality of component types based on the extracted data stored in the extracted data storing means 111 and stores the extraction results in the extracted data storing means 111 .

The recognition frame providing means 102 gives a recognition frame including the electronic/electrical equipment parts waste and the background image around the electronic/electrical equipment parts waste to the electronic/electrical equipment parts waste extracted by the extraction means. By the recognition frame providing means 102 providing the recognition frame including the background image, it becomes easy to recognize the outline of the electronic/electrical equipment component scrap.

The recognition frame providing means 102 preferably provides a circumscribing figure that circumscribes the outline of the electronic/electrical equipment component scrap as a recognition frame. The shape of the circumscribing figure can have any shape such as a circumscribing rectangle, a circumscribing circle, or a circumscribing polygon. Recognition frame providing means 102 provides recognition frames with different characteristics (color, line density, line type (thickness, dotted line/solid line, etc.)) for each component type so that the operator can easily distinguish between a plurality of component scraps. It is desirable to give FIG. 2(a) is a photograph showing an example of a case where substrates are extracted from a captured image as electronic/electrical equipment component scraps. FIG. 2(b) shows an example of a substrate in the upper part of the paper and a plastic extract in the lower part of the paper.

The area estimating means 103 uses the part type area ratio data having at least the information of the area ratio of the electronic/electrical equipment parts scrap to the recognition frame, which is stored in the part area ratio storage means 112, for each of a plurality of component types, Estimate the total area of the electronic/electrical device parts scrap with the recognition frame. The part type area ratio data includes the outline shape information of the electronic/electrical equipment parts scrap, the area of the recognition frame, the area of the electronic/electrical equipment parts scrap within the recognition frame and the area of the background, and the electronic/electrical equipment relative to the recognition frame. It is learning data that can include various information such as information on the area ratio of scrap parts and position information, and that can be updated at any timing by the learning means 107, which will be described later.

The analysis means 104 multiplies the result of estimating the total area of the electronic/electrical device parts scrap for each part type by the assumed weight per unit area for each of a plurality of predetermined part types, and determines whether the total area is included in the plurality of part types. By analyzing the weight of the electronic/electrical device parts scrap, the weight ratio of each of a plurality of types of parts is analyzed, thereby analyzing the composition of the electronic/electrical device parts scrap existing in the captured image.

The assumed weight per unit area of a plurality of component types can be set in advance by the operator via the input device 120 or the like according to the operation results. Although not limited to the following, for example, when electronic and electrical equipment parts scraps are classified into three types of substrates, plastics, and other parts, the assumed weight of substrate scraps is assumed to be 2.0 g/cm ² , and the assumed weight of plastics is 2.0 g/cm 2 . The weight can be set at 1.5 g/cm ² and the other parts at 1.0 g/cm ² .

In addition to the information on the area described above, the analysis means 104 calculates the number of parts constituting the part type extracted for each part type, the area estimation result, and the number of parts. It is also possible to analyze other physical properties such as the average particle diameter and the weight ratio of elements constituting each part type and output them to the display device 130 or the like.

The operating condition generating means 105 generates information on operating conditions of a sorting machine for sorting out a plurality of component types based on the analysis results of the weight ratios of the plurality of component types by the analyzing means 104 . As the sorting machine, there are various sorting machines such as picking, color sorter, metal sorter, eddy current sorter, wind sorter, and sieving machine. For example, the operating condition generating means 105 generates operating conditions for, for example, a color sorter that sorts substrates and plastics from the analysis results by the analyzing means 104 and stores the generated operating conditions in the additional information storage means 114 . The operating conditions stored in the additional information storage means 114 are output to the sorting machines 13 and 14, and the sorting machines 13 and 14 can perform sorting processing according to the output operating conditions.

The position information output means 106 acquires the position information of each of the plurality of component types classified by the extraction means 101 in the image of the electronic/equipment component scrap, and stores the position information in the additional information storage means 114 . Then, the position information is output to specific selectors 13 and 14 for extracting the position of a specific component type from among a plurality of component types and sorting it out. For example, substrates and metal pieces cannot be separated by specific sorters 13 and 14 such as metal sorters, but if individual position information can be obtained from image information, they can be sorted by sorters 13 and 14 having a picking function. will be able to

The learning means 107 creates learning data necessary for extraction processing and estimation processing by machine learning. For example, the learning means 107 learns the characteristics of each of a plurality of component types such as substrates, plastics, metal pieces, and copper wire scraps. With respect to information such as the relationship between, the features are learned based on the input of hundreds to tens of thousands of data, and learning is performed so as to improve the accuracy of extraction processing and estimation processing.

Furthermore, when misrecognition of electronic/electrical equipment component scrap occurs, the learning means 107 learns that the misrecognition or omission of extraction by the extraction means 101 occurs. Learn more about the characteristics of equipment scrap. For example, the learning means 107 creates a new recognition frame including information on the outline of the electronic/electrical equipment parts scrap not extracted by the extracting means 101 and the background image of the electronic/electrical equipment parts scrap not extracted by the extraction process. A new learning model is created by machine learning or the like according to the input of information such as the information of , and the information of the area ratio of electronic/electrical equipment parts scrap to the recognition frame.

Based on the learning result of the learning means 107, the updating means 108 can update the extraction data and the part type area ratio data used by the extraction means 101 to extract the part type. The updated data may be transmitted to the sorter 14 and server 15 connected via the network 11 .

An example of a method for processing electronic/electrical device component scraps using the electronic/electrical device component scrap processing apparatus shown in FIG. 1 will be described with reference to the flowchart of FIG. In step S100, a captured image is acquired. The captured image may be a captured image input via the input device 120 or the network 11, or a captured image captured by the imaging device 12 may be used. In step S101, the extracting means 101 of FIG. (For example, substrates, plastics, metal pieces, copper wire scraps, capacitors, IC chips, and other types of parts are classified into 7 categories).

The classification result by the extraction means 101 can be displayed by the display device 130 or the like. In order to make it easier for the operator to confirm, the classification result is displayed in the image displayed on the display device 130 in different colors for each component type, for example, by using a red frame for boards and a blue frame for plastics. A recognition frame can be attached. At this time, the position information output means of FIG.

For example, when classified into 7 categories: substrates, plastics, metal scraps, copper wire scraps, capacitors, IC chips, and other component types, substrates, copper wire scraps, capacitors, and IC chips are classified as valuables, and metal scraps (aluminum, By optimizing the sorting conditions so that SUS) and plastics are sorted out as smelting inhibitors, it is possible to quantify and manage the separation efficiency, loss rate, and operation results of electronic and electrical equipment parts scraps. .

In step S102, the recognition frame attaching means 102 recognizes a plurality of electronic/deferred equipment parts scraps extracted by the extracting means 101 for each of a plurality of parts types, including a background image surrounding the electronic/electrical equipment parts scraps. give a frame. In step S103, the area estimating means 103, based on the component type area ratio data having the information of the area ratio of the electronic/electrical equipment parts scrap to the recognition frame, for each of a plurality of component types, the electronic/electrical device with the recognition frame attached. Estimate the area (total area) of electrical equipment parts scrap. In step S103, the analysis unit 104 multiplies the result of the area estimation by the area estimation unit 103 by the assumed weight per unit area of the plurality of component types, and analyzes the weight ratios of the plurality of component types. Analyze the composition of electronic and electrical equipment parts scrap in the image. For example, by multiplying the total area of each part type in the image by the weight per unit area, the total weight of scrap parts can be roughly calculated. By comparing the total weight of the parts scrap for each part type, the composition of the electronic/electrical equipment parts scrap contained in the captured image can be analyzed. The analysis result is output in step S104.

According to the composition analysis method and composition analysis apparatus for electronic/electrical device component scraps according to the embodiment of the present invention, a recognition frame including electronic/electrical device component scrap and its surrounding background image in a captured image, preferably an outline rectangle By giving the recognition frame of and analyzing the weight of the electronic / electrical equipment parts scrap for each component scrap using the information on the area ratio of the electronic / electrical equipment parts scrap to the recognition frame, It is possible to obtain the weight ratio for each part type of the electronic/electrical equipment parts scrap present in the . This allows the raw material composition to be rapidly deduced regardless of individual experience and skill. As a result, the operator can make decisions on raw material purchase conditions and raw material selection methods at an early stage, so that the entire factory can be operated more efficiently.

The background image around the electronic/electrical equipment parts scrap may differ depending on the conditions of the imaging destination, and depending on the situation, the extraction means 101 may not be able to extract the electronic/electrical equipment parts scrap appropriately. In the present embodiment, the learning means 107 performs extraction processing of electronic/electrical equipment parts scraps, including background information and new recognition frame information obtained by synthesizing an image obtained by combining an image with borders of electronic/electrical equipment parts scraps. By newly creating learning data for , it is possible to obtain a more flexible composition analysis apparatus that can cope with various conditions in the captured image.

FIG. 4 shows an example of comparison between the result of estimating the area of a board and a plastic as an electronic/electrical device, respectively, extracted from a captured image according to the analysis method according to the embodiment of the present invention and the measured value. It is a table representing As shown in FIG. 4, according to the method for analyzing the composition of electronic and electrical equipment component scraps according to the embodiment of the present invention, the substrate has a measurement error of less than 5.0%, and the plastic has a measurement error of less than 10%. Appropriate evaluation was made with errors, and a recognition rate of 90% or more was achieved when two types of parts were included. Therefore, it can be said that this method is sufficiently effective in numerically grasping the composition of raw materials.

Although the present invention has been described by the above embodiments, the statements and drawings forming part of this disclosure should not be understood to limit the present invention. That is, the present invention is not limited to each embodiment, and can be embodied by modifying the constituent elements without departing from the spirit of the present invention. Moreover, various inventions can be formed by appropriately combining a plurality of constituent elements disclosed in each embodiment. For example, some components may be deleted from all components shown in the embodiments. Furthermore, components of different embodiments may be combined as appropriate.

For example, the analysis means 104 can quantify and analyze the average area, number of parts, average particle size, weight ratio, etc. for each of a plurality of component types from the captured image. The raw material composition can be quantified and grasped much more quickly than the evaluation of the raw material composition of electronic and electrical equipment parts scrap.

Furthermore, based on the raw material analysis result analyzed by the analysis means 104, information on the operating conditions of a sorter for sorting out a specific part type from among a plurality of component types, for example, selection of a sorter for sorting raw materials Then, operating conditions such as sorting conditions and sorting order can be determined, and sorting processing can be performed based on the operating conditions.

For example, to increase the weight ratio of substrates and plastics in the processed materials after sorting by sorting electronic and electrical equipment parts scraps into light and heavy items by wind sorting using a wind sorter. can be processed. In this case, in the processing by the sorters 13 and 14, the air volume of the wind sorter can be adjusted according to the average particle size of the plurality of component types based on the raw material analysis result analyzed by the analysis means 104. The air volume can be, for example, about 5 to 20 m/s, more preferably about 5 to 12 m/s, furthermore about 5 to 10 m/s. The wind sorting can be repeated two or more times according to the raw material analysis result analyzed by the analysis means 104 .

Alternatively, by performing a picking process using a picking device before carrying out the above wind sorting, it is possible to perform a picking process for removing clumps of copper wire scraps. In this picking process, the position information of the scrap copper wire stored in the additional information storage means 114 is output to the picking device as the sorter 13, and the picking device can remove the scrap copper wire according to the output result. . This copper wire scrap can be sent, for example, to a valuable metal recovery process.

When the wind sorting is repeated two or more times, a sorting process using a sieving machine can be performed between the first wind sorting and the second wind sorting. In this case, a sieving machine is adopted as the sorting machine 13, and the sieving machine for sorting the characteristic part type based on the average particle size of a plurality of part types, which is the raw material analysis result analyzed by the analysis means 104. The size of the sieve mesh can be changed.

In addition to the methods described above, the present invention is applied to the sorting machine 13 used in the magnetic sorting process, the eddy current sorting process, and the optical sorting process for optically sorting metallic objects and non-metallic objects. By utilizing the composition analysis results obtained by the composition analysis apparatus according to the embodiment, the raw material composition can be analyzed by analyzing the image data in real time while continuously photographing electronic and electrical device parts scraps being transported. can be done.

Conventionally, the raw material composition of electronic and electrical equipment parts scrap was evaluated by visual judgment and chemical analysis, and the results were reflected in the operation management of the sorting process and the setting of operating conditions. In the method of grasping the composition of the raw material by the method, it was not possible to perform rapid processing.

According to the embodiment of the present invention, the composition of electronic/electrical device parts whose composition changes from moment to moment is instantaneously determined by image analysis and separation based on predetermined classification data. Since it can be quantified, it is possible to quickly sort a large amount of electronic and electrical equipment parts scrap under more appropriate conditions.

Furthermore, by image analysis of the raw material composition of scrap parts before and after processing by the sorters 13 and 14, the sorting efficiency (performance) of the sorters 13 and 14 can be evaluated based on the amount of change in the scrap parts. By identifying the raw material composition of electronic and electrical equipment parts scraps and extracting their position information, and by interlocking with sorting machines 13 and 14 such as picking devices, color sorters, and metal sorters, it becomes easy to separate individual parts types. . In addition, since the analysis result is displayed on the display device 130 with a frame of a different color for each raw material type, it is easier for the operator to recognize it, so it is easier to recognize erroneous detection by the composition analysis device.

DESCRIPTION OF SYMBOLS 10... Composition-analysis apparatus 11... Network 12... Imaging apparatus 13, 14... Sorter 15... Server 100... Control part 101... Extraction means 102... Recognition frame provision means 103... Area estimation means 104... Analysis means 105... Operating condition generation means 106 Position information output means 107 Learning means 108 Updating means 110 Storage device 111 Extracted data storage means 112 Part area ratio storage means 113 Analysis information storage means 114 Additional information storage means 120 Input device 130 Display Device

Claims (7)

Extracting the electronic/electrical device parts waste for each of the plurality of component types from among the captured images obtained by imaging a plurality of electronic/electrical device component wastes including a plurality of component types,
Giving a recognition frame including a background image of the electronic/electrical device parts and surrounding the electronic/electrical device parts to the extracted electronic/electrical device parts,
Based on part type area ratio data, which is learning data stored in advance in a storage means and which has at least information on the area ratio of the electronic/electrical device parts scrap with respect to the recognition frame, the recognition is performed for each of the plurality of component types. Estimate the total area of the framed electronic and electrical equipment parts scrap,
By multiplying the estimation result of the total area by the assumed weight per unit area for each of the plurality of component types, and analyzing the weight ratio of the electronic/electrical device parts scrap for each of the plurality of component types, the A composition analysis method for electronic/electrical device parts scrap, comprising: analyzing a composition of the electronic/electrical device parts scrap in a captured image. information on the outline of the electronic/electrical device parts scrap in the captured image that is not extracted in the extraction process, and the electronic/electrical component scrap in the captured image that is not extracted in the extraction process and the background image; and information on the area ratio of the electronic/electrical device parts scrap to the recognition frame, and performing the extraction process and the estimation process based on the acquisition result. The composition analysis method for electronic/electrical equipment parts scrap according to claim 1. 2. The method for compositional analysis of electronic and electrical equipment scraps according to claim 1, wherein said plurality of component types include at least substrates and plastics. The claim further comprising generating information on operating conditions of a sorter for sorting out a specific component type from among the plurality of component types, based on the analysis result of the composition of the electronic/electrical device component scrap. 4. The method for analyzing the composition of electronic/electrical equipment parts scraps according to any one of 1 to 3. A selection step of selecting a specific component type from among the plurality of component types based on the analysis result of the composition of the electronic/electrical device component scrap according to any one of claims 1 to 4. A method for processing electronic and electrical equipment parts scraps. an extracting means for extracting the electronic/electrical device parts scrap from a captured image obtained by imaging a plurality of electronic/electrical device component scraps including a plurality of component types;
a recognition frame adding means for adding a recognition frame including the electronic/electrical equipment parts scrap and a background image around the electronic/electrical equipment parts scrap to the extracted electronic/electrical equipment parts scrap;
The recognition frame for each of the plurality of component types based on component type area ratio data , which is learning data stored in advance in a storage means and has information on the area ratio of the electronic/electrical device parts scrap with respect to the recognition frame. an area estimating means for estimating the total area of the electronic/electrical device parts scrap marked with
By multiplying the estimation result of the total area by the assumed weight per unit area for each of the plurality of component types, and analyzing the weight ratio of the electronic/electrical device parts scrap for each of the plurality of component types, the and an analysis means for analyzing the composition of the electronic/electrical device component waste in the captured image. an imaging means for imaging a plurality of electronic/electrical device component scraps including a plurality of component types;
extracting means for extracting the electronic/electrical equipment parts scrap from the picked-up image; Recognition frame assigning means for assigning a recognition frame including an image, based on part type area ratio data , which is learning data stored in advance in a storage unit, having information on the area ratio of the electronic/electrical equipment parts scrap to the recognition frame. area estimating means for estimating the total area of the electronic/electrical device component scrap to which the recognition frame is attached for each of the plurality of component types; By multiplying the estimated weight per unit area and analyzing the weight ratio of the electronic/electrical device parts scrap for each of the plurality of component types, the composition of the electronic/electrical device parts scrap in the captured image is determined. a composition analysis device comprising analysis means for analysis;
A processing apparatus for electronic/electrical equipment parts scraps, comprising: a sorter for sorting out specific parts scraps from the electronic/electrical equipment parts scraps based on the composition analysis result analyzed by the composition analysis device.

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