TWI805513B - Intelligent bottle recycling identification method - Google Patents

Abstract

The present invention provides a method for recycling and identifying smart bottle shapes, which is executed by a recycling machine. The recycling machine is provided with an image capture module and a processing module. The method for recycling and identifying smart bottle shapes includes the following steps: image capture The fetching module captures the image of the article to be recycled and obtains an original image; the processing module receives the original image and cuts the background of the original image to obtain a processed image; and then processes the article to be recycled in the first processed image Perform edge detection and obtain an edge image and define a positioning frame around the edge image, and form the positioning frame on the first processed image; define a first reference area in the positioning frame of the first processed image and A second reference area and judging whether the direction of the object to be recycled is correct based on the images in the first reference area and the second reference area; when the direction of the object to be recycled is correct, the processing unit defines the positioning frame in the first processed image. At least one partial frame, the color of the item to be recycled is judged by the color in the partial frame, and a color setting condition is used to determine whether the item to be recycled is recycled; and when the color of the item to be recycled meets the color condition setting, an artificial intelligence model is used Identify the items to be recycled, so as to achieve the effect of accurately judging the recycled items and saving manpower and time.

Description

Intelligent bottle recycling identification method

The invention relates to a recycling method, in particular to a smart bottle type recycling identification method that can accurately judge recycled objects to save manpower and time.

With the continuous improvement of the quality of life, people pay more and more attention to environmental protection, and the most important thing in environmental protection is the classification and recycling of waste. Different wastes are collected and reused by classification, and have been decomposed and remanufactured into environmentally friendly products to achieve resource reuse. And reduce the consumption of raw materials.

The government has announced that Taiwan will have net-zero carbon emissions by 2025. Throw the general recycling garbage into the trash can, and then you need to hire a special person to sort it out and recycle it. Therefore, the labor cost of transportation will be relatively incurred in these recycling processes, and a lot of carbon footprint will also be generated in the recycling process, because the recycling plant usually recycles Garbage needs to be sorted by manpower or machines. During the process of machine sorting, waste water, waste heat and waste carbon will be generated. The reduction of waste carbon is very helpful to the global environment.

Plastic bottles are convenient to use, so most beverages are stored in plastic bottles or plastic cups, but as the usage increases, the amount of waste will increase. Due to the characteristics of plastics that are not easy to decompose, the plastic Recycling and post-recycling processing methods have become extremely important, and the current common recycling method is usually to sort and recycle manually, which not only causes manpower loss, but also takes a lot of time.

Therefore, how to solve the above-mentioned conventional problems and deficiencies is the direction that the inventor of the present invention and related manufacturers engaged in this industry want to research and improve urgently.

Therefore, in order to effectively solve the above-mentioned problems, the main purpose of the present invention is to provide a smart bottle type recycling identification method that can accurately judge recycled objects and save manpower and time.

In order to achieve the above purpose, the present invention provides a smart bottle type recycling identification method, which is implemented by a recycling machine. The recycling machine is equipped with an image capture module and a processing module. The smart bottle type recycling identification method includes The following steps: the image capture module captures the image of the article to be recycled and obtains an original image; the processing module receives the original image and cuts the background of the original image to obtain a processed image; and then processes the first processed image Perform edge detection on the items to be recycled and obtain an edge image and define a positioning frame on the periphery of the edge image, and form the positioning frame on the first processed image; define a positioning frame in the first processed image A first reference area and a second reference area, and judge whether the direction of the article to be recycled is correct based on the images in the first reference area and the second reference area; There is at least one partial frame defined in the positioning frame, the color of the article to be recycled is determined by the color in the partial frame, and a color setting condition is used to determine whether the article to be recycled is recycled; and when the color of the article to be recycled meets the color condition setting, then An artificial intelligence model is used to identify the item to be recycled.

The present invention also discloses a smart bottle type recycling identification method, wherein the processing module performs grayscale processing and image blurring processing on the first processed image, and performs edge detection on the image after grayscale processing and image blurring processing to obtain the edge image.

The present invention also discloses a smart bottle type recycling and identification method, wherein noise filtering and pixel expansion processing are performed simultaneously during the edge detection.

The present invention also discloses a smart bottle type recycling identification method, wherein the processing module performs pixel erosion processing and pixel expansion processing on the edge image to obtain a binary image, and searches for peripheral white points in the binary image and records its four corners Then classify the white point below the set pixel as noise and eliminate it, and the periphery generated by the upper left corner as the starting coordinate and the lower right corner as the maximum coordinate is defined as the positioning frame.

The present invention also discloses a smart bottle type recycling identification method, wherein the processing module performs pixel expansion processing on the images in the first reference area and the second reference area and calculates the pixel height, if the image height in the first reference area If it is greater than the image height of the second reference area, the processing module judges that the orientation of the recycled item is correct; if the image height in the first reference area is smaller than the image height of the second reference area, the processing module determines that the orientation of the recycled item is wrong .

The present invention also discloses a smart bottle type recycling identification method, wherein the processing module returns the item to be recycled when it judges that the direction of the item to be recycled is wrong based on the images in the first reference area and the second reference area.

The present invention also discloses a smart bottle-type recycling identification method, wherein when the direction of the item to be recycled is correct, the processing unit first searches the outer packaging of the item to be recycled with a contour search range for the first processed image, and searches for the outer packaging of the item to be recycled with a search condition The outline of the article in the outer package, and the partial frame is defined by the outline of the article.

The present invention also discloses a smart bottle type recycling identification method, wherein the color setting conditions are white and green to determine that the item to be recycled is to be recycled, and the color setting conditions are black and blue to determine that the item to be recycled is returned.

The present invention also discloses a smart bottle type recycling identification method, wherein the artificial intelligence model identifies the material and color of the items to be recycled.

The present invention also discloses a smart bottle type recycling identification method, wherein the artificial intelligence model recognizes that the item to be recycled is bottle-shaped and made of PET material, and then recycles it. If it recognizes that the item to be recycled is cup-shaped and made of PP material or color is When it is green, it is judged that the item to be recycled is returned.

The present invention also discloses a smart bottle type recycling identification method, wherein before the processing module obtains the first processed image, the processing module first identifies whether the original image is a plastic bottle, and if it is a plastic bottle, then obtains the First process the image, and return the item to be recycled if it is not a PET bottle.

The present invention also discloses a smart bottle recycling identification method, wherein the recycling machine further includes a weight sensing module, the weight sensing module is electrically connected to the processing module, and the weight sensing module is The weight of the item to be recycled is sensed and a weight sensing signal is generated and transmitted to the processing module.

The present invention also discloses a smart bottle type recycling identification method, wherein the recycling machine further includes a metal sensing module, the metal sensing module is electrically connected to the processing module, and the metal sensing module is The object to be recycled is sensed and a metal sensing signal is generated and transmitted to the processing module.

The above-mentioned purpose of the present invention and its structural and functional characteristics will be described based on the preferred embodiments of the accompanying drawings.

In the following, for the composition and technical content of the intelligent bottle type recycling identification method of the present invention, various applicable examples are listed and described in detail with reference to the accompanying drawings; however, the present invention is certainly not limited to the listed These embodiments, drawings, or detailed descriptions are just for reference.

Furthermore, those who are familiar with this technology should also understand that the listed embodiments and accompanying drawings are only for reference and description, and are not used to limit the present invention; they can be easily implemented based on these records. Inventions completed through modification or alteration are also considered within the scope of not departing from the spirit and intent of the present invention, and of course such inventions are also included in the scope of the patent application of the present invention.

In addition, the directional terms mentioned in the following embodiments, such as "upper", "lower", "left", "right", "front", "rear", etc., are only referring to the directions attached to the drawings. Therefore, the used directional terms are used to illustrate rather than limit the present invention; moreover, in the following embodiments, the same or similar components will use the same or similar component numbers.

Please refer to Figure 1 and Figure 2 at the same time, which are the flow chart of the smart bottle type recycling identification method of the present invention and the hardware structure diagram of the recycling machine, wherein the smart bottle type recycling identification method of the present invention is mainly applied to those with A recycling machine 1, the recycling machine 1 is provided with an image capture module 11, a processing module 12, at least one control module 13, an artificial intelligence model 14, a weight sensing module 15 and a metal sensor Measurement module 16, the control module 13 is provided with at least one backlight assembly 131 and at least one conveying assembly 132, wherein the processing module 12 is signal-connected to the image capture module 11, the control module 13 and the artificial intelligence Model 14 and the weight sensing module 15 and metal sensing module 16, and the method of intelligent bottle type recycling identification method is as follows:

Step S1: The image capture module captures the image of the article to be recycled and obtains an original image; when the article to be recycled is put into the conveying unit 132 of the recycling machine 1, the control module 13 controls the movement of the conveying unit 132 and starts The backlight module, and the conveying unit 132 sends the article to be recycled to the front of the backlight module, and the image capture module 11 captures the image of the article to be recycled and obtains an original image P1, such as As shown in FIG. 3 , the original image P1 is an image of the article to be recycled and a part of the recycling machine 1 , and the image capture module 11 sends the original image P1 to the processing module 12 .

Step S2: The processing module receives the original image and cuts the background of the original image to obtain a processed image; after the processing module 12 receives the original image P1, the processing module 12 will internally The storage unit is identified, and the storage unit stores the appearance data and material data of multiple PET bottles or other bottle types, and the processing module 12 will first pass the storage data in the storage unit and the original image P1 performs comparison and identification. If it is identified that the item to be recycled is not a plastic bottle, the processing module 12 transmits a control signal to the control module 13, and the control module 13 controls the conveying component 132 through the control signal. Turn and return the items to be recycled.

The processing module 12 compares and identifies whether the article to be recycled is a plastic bottle, and includes an action of additional weight judgment, wherein the storage unit of the processing module 12 also stores the relative size of each bottle. Therefore, when the processing module 12 compares and identifies the original image P1 through the stored data in the storage unit, the processing module 12 will also first know the bottle through the data of the bottle type The relative weight range of the type, and the weight sensing module 15 senses the weight of the article to be recycled and generates a weight sensing signal and sends it to the processing module 12, and the processing module 12 receives the weight sensing The signal is compared with the relative weight range of the bottle type. If the weight sensing signal matches the relative weight range, it means that the item to be recycled is the set plastic bottle and there is no foreign matter in it. It is an empty bottle and Recycling, on the contrary, if the weight sensing signal does not match the relative weight range, it means that the item to be recycled is not a plastic bottle and there are foreign objects in it, not an empty bottle, and the item to be recycled will be returned and recycled.

In addition, the processing module 12 can not only judge whether the article to be recycled is a plastic bottle through the appearance, material and weight, but also can identify whether the article to be recycled is a plastic bottle or a metal bottle or has a Metal foreign objects, wherein the metal sensing module 16 senses whether the object to be recycled has a metal material, and if it senses that the object to be recycled has a metal material, it means that the object to be recycled is made of metal or internal There is a metal foreign object, and the metal sensing module 16 generates a metal sensing signal and transmits it to the processing module 12, and the processing module 12 determines that the article to be recycled is a metal foreign object after receiving the metal sensing signal Metal material or metal foreign objects inside and return the items to be recycled.

If the processing module 12 recognizes that the article to be recycled is a plastic bottle and there is no foreign matter inside, the processing module 12 cuts (ROI) the redundant background image of the original image P1, and the processing module 12 cuts it first The redundant background image can reduce the amount of subsequent calculations, and the processing module 12 can obtain a processed image P2 by cutting the redundant background image, as shown in FIG. 4 .

7的核心大小進行影像模糊處理，如第5B圖所示，使像素強度均勻柔和，而後再將灰階處理及影像模糊處理後的影像以Canny邊緣檢測法進行邊緣檢測，而其係使用參數門檻24，以輸出帶有白色邊緣的二進制影像，如第5C圖所示，又其中，在邊緣檢測時，該處理模組12同時進行雜訊濾除處理及像素膨脹處理，其中該雜訊濾除處理主要是避免下方及四周雜訊白線條干擾，所以將其填補為0並進行濾除，如第5D圖所示，而於雜訊濾除後之白色像素再進行7

7的核心大小之膨脹處理，使其白色像素更加凸顯，如第5E圖所示，且該處理模組12透過其白色像素取得所述邊緣影像P3，而該處理模組12在取得邊緣影像P3後，該處理模組12為了將邊緣影像P3中之小雜點濾除且不破壞待回收物品的輪廓結構，該處理模組12便對其邊緣影像P3進行2

2的核心大小之像素侵蝕處理及進行20

20的核心大小之像素膨脹處理取得一二值影像，如第5F圖所示，且該處理模組12尋找二值影像中的外圍白點且記錄其四角座標點，且通過面積的計算，將1000像素以下的雜訊給排除，如第5G圖之黃框及第6圖所示，而圖中之藍色區域則為待回收物品之主要區塊，而該處理模組12於藍色區域擷取出後，將其左上角為起始座標與右下角為最大座標所產生的外圍界定為所述定位框F1，且該處理模組12將所述定位框F1形成於所述首處理影像P2上，如第6圖所示，而於本實施例中，係以處理模組12取得所述首處理影像P2後，該處理模組12將該首處理影像P2中之待回收物品進行邊緣檢測為主要實施方式，其中任何可以從首處理影像P2中取得邊緣影像P3的方式皆為本案保護範圍。 Step S3: Perform edge detection on the article to be recycled in the first processed image, obtain an edge image and define a positioning frame around the edge image, and form the positioning frame on the first processed image; the processing model After the group 12 obtains the first processed image P2, the processing module 12 performs edge detection on the articles to be recycled in the first processed image P2, and when detecting the edge, in this embodiment, the processing module 12 first Grayscale processing is performed on the first processed image P2, and the original RGB image is converted into a grayscale image, as shown in FIG. 5A, and the processing module 12 blurs the grayscale image by 7

The core size of 7 is used for image blur processing, as shown in Figure 5B, to make the pixel intensity uniform and soft, and then the image after gray scale processing and image blur processing is used for edge detection by Canny edge detection method, which uses the parameter threshold 24, to output a binary image with white edges, as shown in Figure 5C, and wherein, during edge detection, the processing module 12 simultaneously performs noise filtering processing and pixel expansion processing, wherein the noise filtering The main purpose of the processing is to avoid the interference of white lines of noise below and around, so it is filled with 0 and filtered, as shown in Figure 5D, and the white pixels after noise filtering are then processed by 7

The expansion processing of the kernel size of 7 makes its white pixels more prominent, as shown in Figure 5E, and the processing module 12 obtains the edge image P3 through its white pixels, and the processing module 12 obtains the edge image P3 Afterwards, in order to filter out the small noise points in the edge image P3 without destroying the outline structure of the article to be recycled, the processing module 12 performs 2 operations on the edge image P3.

Pixel erosion processing with a core size of 2 and 20

A binary image is obtained by pixel expansion processing with a core size of 20, as shown in FIG. 5F, and the processing module 12 searches for peripheral white points in the binary image and records its four-corner coordinate points, and by calculating the area, the The noise below 1000 pixels is eliminated, as shown in the yellow frame of Figure 5G and Figure 6, and the blue area in the figure is the main block of the object to be recycled, and the processing module 12 is in the blue area After extraction, define the periphery generated by the upper left corner as the starting coordinate and the lower right corner as the maximum coordinate as the positioning frame F1, and the processing module 12 forms the positioning frame F1 on the first processed image P2 Above, as shown in Figure 6, in this embodiment, after the processing module 12 obtains the first processed image P2, the processing module 12 performs edge detection on the items to be recycled in the first processed image P2 It is the main implementation mode, and any method that can obtain the edge image P3 from the first processed image P2 is within the scope of protection of this case.

Step S4: Defining a first reference area and a second reference area in the positioning frame of the first processed image and judging whether the direction of the object to be recycled is correct for the images in the first reference area and the second reference area; the processing model After group 12 forms the positioning frame F1 on the first processed image P2, the processing module 12 defines a first reference area B1 and a second reference area B2 within the positioning frame F1 of the first processed image P2 , wherein the first reference area B1 is the left side of the positioning frame F1, and in this embodiment, the left side of the positioning frame F1 is moved to the right by 130 pixels and the area in between is defined as the first reference area B1, wherein the The second reference area B2 is the right side of the positioning frame F1, and it moves the right side of the positioning frame F1 to the left by 130 pixels and defines the area in between as the second reference area B2, and then cuts the first reference area B1 and The area outside the second reference area B2, and the processing module 12 performs pixel expansion processing on the images in the first reference area B1 and the second reference area B2 and calculates the pixel height, if the height of the image in the first reference area B1 If it is greater than the image height of the second reference area B2, the processing module 12 judges that the direction of the recycled article is correct; if the image height in the first reference area B1 is smaller than the image height of the second reference area B2, the processing module 12 judges The recycling item is in the wrong direction, and the item to be recycled is returned, as shown in Figures 7A and 7B, and in this embodiment, it is judged based on the height comparison between the first reference area B1 and the second reference area B2 Whether the direction of the article to be recycled is correct is the main implementation method, and any method that can determine the direction of the article to be recycled is within the scope of protection of this case.

定位框F1X軸最小值-50pixel、輪廓範圍X軸最大值

定位框F1X軸最大值+60pixel、輪廓範圍Y軸最小值

定位框F1Y軸最小值-50pixel、輪廓範圍Y軸最大值

定位框F1Y軸最大值+60pixel，所以該處理單元透過所述搜索條件取得該待回收物品之物品輪廓，而其物品輪廓可為底部凹槽輪廓或包裝輪廓或頭部輪廓，而該處理單元便可由該影像判斷出該物品輪廓數量。 Step S5: When the direction of the article to be recycled is correct, the processing unit then defines at least one partial frame in the positioning frame in the first processed image, determines the color of the article to be recycled based on the color in the partial frame, and sets a color to determine the condition Whether the article to be recycled is to be recycled or not; when the processing module 12 judges that the direction of the article to be recycled is correct, the processing module 12 defines at least one local frame F2 in the positioning frame F1 in the first processed image P2, and the local Before the frame F2 is defined, the processing unit searches the first processed image P2 with a contour search range to search for the outer packaging of the article to be recycled, wherein the minimum value of the contour search range is defined as the minimum value of the X-axis of the positioning frame F1 in this embodiment Coordinates + positioning frame F1 width/5, and the maximum value of the contour search range are defined as the positioning frame F1 X axis maximum coordinates + positioning frame F1 width/4, but not limited thereto, so the processing unit passes through the contour search range To find out the possible outline range of the article to be recycled, the processing unit searches the outline of the article to be recycled with a search condition, wherein the first condition of the search condition in this embodiment is that the length of the outline is greater than 10pixel and Only when the width is greater than 10pixels, the processing unit will set the target as the outline of the object to be recycled, but not limited to this. After the outline is set, search conditions are used to capture the outline position, and the outline position The search condition is the minimum value of the contour range X axis

The minimum value of the positioning frame F1 X-axis -50pixel, the maximum value of the contour range X-axis

Positioning frame F1 X-axis maximum value +60pixel, outline range Y-axis minimum value

The minimum value of the F1 Y axis of the positioning frame -50pixel, the maximum value of the Y axis of the outline range

The maximum value of the F1Y axis of the positioning frame is +60pixel, so the processing unit obtains the item outline of the item to be recycled through the search conditions, and the item outline can be the bottom groove outline or the package outline or the head outline, and the processing unit then The outline quantity of the object can be judged from the image.

2時，其物品輪廓由左至右之順序則可為包裝輪廓及頭部輪廓、包裝輪廓及包裝輪廓及頭部輪廓、底部凹槽輪廓及包裝輪廓及頭部輪廓，並於輪廓數量取得後，該處理單元會再計算位置偏差值來產生至少一局部框F2，而最左邊的局部框F2其位置偏差值可舉例為在100pixel，位置偏差值（輪廓範圍X軸之位置-定位框F1X軸最小值）

50pixel之狀況下產生有局部框F2，並該處理單元計算該局部框F2所要產生之位移量，如100pixel/4＝移動25pixel，而局部框F2之位置與大小則為：局部框F2X軸最小值＝定位框F1X軸最小值+25pixel、與局部框F2X軸最大值＝輪廓範圍X軸最小值-25pixel。 If the item outline quantity

2, the order from left to right of the outline of the article can be the outline of the package and the head, the outline of the package and the outline of the head, the outline of the bottom groove, the outline of the package and the head, and after the number of outlines is obtained , the processing unit will then calculate the position deviation value to generate at least one local frame F2, and the position deviation value of the leftmost local frame F2 can be, for example, at 100 pixels, the position deviation value (the position of the X-axis of the contour range-the position of the F1 X-axis of the positioning frame min)

In the case of 50 pixels, a local frame F2 is generated, and the processing unit calculates the displacement amount of the local frame F2, such as 100pixel/4 = moving 25 pixels, and the position and size of the local frame F2 are: the minimum value of the X axis of the local frame F2 = The minimum value of the F1 X axis of the positioning frame + 25pixel, and the maximum value of the F2 X axis of the local frame = The minimum value of the X axis of the outline range - 25pixel.

8pixel內時，則產生有局部框F2；若位置X軸最小值在輪廓範圍內時，則該設定位置偏差值在

90pixel內時，則產生有局部框F2，而局部框F2之位置與大小則為：局部框F2X軸最小值＝上一個局部框F2X軸最大值+25pixel、與局部框F2X軸最大值＝輪廓範圍X軸最小值-25pixel，如第8A圖所示。 The position deviation value of the rightmost local frame F2 is the minimum value of the X axis of this position - the maximum value of the X axis of the previous local frame F2, and the processing unit calculates the displacement to be generated by the local frame F2, such as 100pixel/4=move 25pixel, and there can also be a setting condition, if the minimum value of the X-axis of the position is not within the contour range, then the set position deviation value is within

If it is within 8pixel, a local frame F2 will be generated; if the minimum value of the position X axis is within the contour range, the set position deviation value is within

When within 90 pixels, there is a local frame F2, and the position and size of the local frame F2 are: the minimum value of the F2X axis of the local frame = the maximum value of the F2X axis of the previous local frame + 25pixel, and the maximum value of the F2X axis of the local frame = the outline range The minimum value of the X axis is -25pixel, as shown in Figure 8A.

50pixel之狀況下產生有局部框F2，並該處理單元計算該局部框F2所要產生之位移量，如100pixel/4＝移動25pixel，而局部框F2之位置與大小則為：局部框F2X軸最小值＝輪廓範圍X軸最小值+25pixel、與局部框F2X軸最大值＝定位框F1X軸最大值-25pixel，若位置偏差值（輪廓範圍X軸之位置-定位框F1X軸最小值）

50pixel之狀況下，則計算其整個待回收物品之有局部框F2，而局部框F2之位置與大小則為：局部框F2X軸最小值＝定位框F1X軸最小值、局部框F2X軸最大值＝定位框F1X軸最大值，如第8B圖所示，而其中局部框F2之限制條件與計算方式並不因此為限，僅要在定位框F1內界定有局部框F2皆為本案保護範圍。 If there is only one outline of the item, the position deviation value = the minimum value of the F1X axis of the positioning frame - the maximum value of the X axis of the contour range, and the position deviation value (the position of the X axis of the contour range - the minimum value of the F1X axis of the positioning frame)

In the case of 50 pixels, a local frame F2 is generated, and the processing unit calculates the displacement amount of the local frame F2, such as 100pixel/4 = moving 25 pixels, and the position and size of the local frame F2 are: the minimum value of the X axis of the local frame F2 =Minimum value of the X-axis of the contour range+25pixel, and the maximum value of the F2X-axis of the local frame=The maximum value of the F1X-axis of the positioning frame-25pixel, if the position deviation value (the position of the X-axis of the contour range-the minimum value of the F1X-axis of the positioning frame)

In the case of 50 pixels, calculate the local frame F2 of the entire item to be recycled, and the position and size of the local frame F2 are: the minimum value of the X axis of the local frame F2 = the minimum value of the X axis of the positioning frame F1, the maximum value of the X axis of the local frame F2 = The maximum value of the X-axis of the positioning frame F1 is shown in Figure 8B, and the constraints and calculation methods of the local frame F2 are not limited thereto. As long as the local frame F2 is defined within the positioning frame F1, it is within the scope of protection of this case.

And after the processing unit defines a partial frame F2, the processing unit judges the color of the article to be recycled by the color in the partial frame F2 and judges whether the article to be recycled is recycled by a color setting condition, for example, the color setting condition The item to be recycled is judged as white and green for recycling, and the color setting condition is black and blue to judge that the item to be recycled is returned, and the setting of the color condition is not limited to this, the color set in the color setting condition can be Set according to the needs of users. For example, if the color setting condition is black and blue, it is judged that the item to be recycled is recycled, while the color setting condition is white and green, it is judged that the item to be recycled is returned.

Step S6: When the color of the item to be recycled meets the color condition setting, an artificial intelligence model is used to identify the item to be recycled.

When the processing unit determines that the color of the item to be recycled meets the color condition setting, the processing unit then transmits the first processed image P2 to the artificial intelligence model 14. The artificial intelligence model 14 is based on a yolo-like neural model architecture, and its main After learning the PET type bottle type, PP type bottle type and green type, and the artificial intelligence model 14 receives the first processed image P2, the artificial intelligence model 14 recognizes the first processed image P2, so as to Recycled items are finally identified. The artificial intelligence model 14 recognizes that the item to be recycled is bottle-shaped and made of PET material, and then recycles it. If it recognizes that the item to be recycled is cup-shaped and made of PP material or the color is green, it is judged that the item to be recycled is returned. , as shown in Figure 9.

Therefore, in this embodiment, the plastic bottle material of the items to be recycled recovered by the smart bottle type recycling identification method needs to be a transparent PET material and there must be no liquid or foreign matter inside, but the bottle cap and the coating of the plastic bottle are not the same. Under the limitation of light transmittance, plastic bottles that have been stepped on are also recyclable. In addition, in this embodiment, transparent PET material is used as the main implementation method, but it is not limited to this, mainly according to The processing module 12 sets the type of recycled items. For example, it can also be set to only recycle PP bottles and cans. The main reason is that the recycled materials can be changed by setting the material spectrum through the processing module 12, so as to achieve accurate judgment. Recycling can save manpower and time consumption.

The present invention has been described in detail above, but the above is only a preferred embodiment of the present invention, and should not limit the scope of the present invention, that is, all equivalent changes and modifications made according to the application scope of the present invention etc., should still fall within the scope of the patent coverage of the present invention.

1: Recycling machine 11: Image capture module 12: Processing module 13: Control module 131:Backlight assembly 132: Conveying components 14: Artificial intelligence model 15: Weight sensing module 16: Metal sensing module P1: Original image P2: First processing image P3: Edge image F1: positioning frame B1: the first reference area B2: Second base area F2: Local frame S1～S6: steps

Fig. 1 is a flow chart of the intelligent bottle recycling identification method of the present invention. Figure 2 is a schematic diagram of the hardware structure of the recycling machine of the present invention. Figure 3 is a schematic diagram of the original image of the present invention. Fig. 4 is a schematic diagram of the first processed image of the present invention. Figures 5A-5G are schematic diagrams of edge image generation according to the present invention. Figure 6 is a schematic diagram of the creation of the positioning frame of the present invention. Figures 7A-7B are schematic diagrams of the direction judgment of the present invention. Figures 8A-8B are schematic diagrams of partial frame generation in the present invention. Fig. 9 is a schematic diagram of artificial intelligence model identification of the present invention.

S1~S6: steps

Claims (12)

A recycling identification method for smart bottles is executed by a recycling machine, the recycling machine is provided with an image capture module and a processing module, the smart bottle recycling identification method includes the following steps: the image capture module captures Get the image of the article to be recycled and obtain an original image; the processing module receives the original image and cuts the background of the original image to obtain a processed image; then performs edge detection on the article to be recycled in the first processed image and Obtain an edge image and define a positioning frame around the edge image, and form the positioning frame on the first processed image; define a first reference area and a second reference in the positioning frame of the first processed image area and judge whether the direction of the object to be recycled is correct based on the images in the first reference area and the second reference area; if the direction of the object to be recycled is correct, the processing unit first searches the first processed image for the object to be recycled with a contour search range Outer packaging, and search for the outline of the article in the outer packaging with a search condition, and then define at least one partial frame in the positioning frame in the first processed image according to the outline of the article, and determine the color of the article to be recycled by the color in the partial frame and A color setting condition is used to judge whether the item to be recycled is to be recycled; and when the color of the item to be recycled meets the color condition setting, an artificial intelligence model is used to identify the item to be recycled. The smart bottle type recycling identification method as described in claim 1, wherein the processing module performs grayscale processing and image blurring processing on the first processed image, and performs edge detection on the image after grayscale processing and image blurring processing to obtain the obtained result. Describe the edge image. The smart bottle recycling and identification method as described in Claim 2, wherein noise filtering and pixel expansion processing are performed simultaneously during edge detection. The smart bottle type recycling identification method as described in claim 1, wherein the processing module performs pixel erosion processing and pixel expansion processing on the edge image to obtain a binary image, and finds and records peripheral white points in the binary image For the four corner coordinate points, the white point below the set pixel is classified as noise and eliminated, and the periphery generated by the upper left corner as the starting coordinate and the lower right corner as the maximum coordinate is defined as the positioning frame. The smart bottle type recycling identification method as described in claim 1, wherein the processing module performs pixel expansion processing on the images in the first reference area and the second reference area and calculates the pixel height, if the image in the first reference area When the image height is greater than the image height of the second reference area, the processing module judges that the direction of the recycled item is correct; if the image height in the first reference area is smaller than the image height of the second reference area, the processing module determines that the recycled item wrong direction. The smart bottle type recycling identification method as described in claim 5, wherein the processing module returns the item to be recycled when it determines that the direction of the item to be recycled is wrong based on the images in the first reference area and the second reference area. The smart bottle type recycling identification method as described in claim 1, wherein the color setting condition is white and green to determine that the item to be recycled is recycled, and the color setting condition is black and blue to determine that the item to be recycled is returned. The intelligent bottle type recycling identification method as described in Claim 1, wherein the artificial intelligence model identifies the material and color of the items to be recycled. The intelligent bottle type recycling identification method as described in claim 8, wherein the artificial intelligence model recognizes that the item to be recycled is in the shape of a bottle and is made of PET material, and if it recognizes that the item to be recycled is in the shape of a cup and is made of PP material or When the color is green, it is judged that the item to be recycled is returned. The smart bottle type recycling identification method as described in claim 1, wherein before the processing module obtains the first processed image, the processing module first identifies whether the original image is a plastic bottle, and if it is a plastic bottle, obtains If the first processed image is not a plastic bottle, the item to be recycled will be returned. The smart bottle type recycling identification method as described in claim 1, wherein the recycling machine further includes a weight sensing module, the weight sensing module is electrically connected to the processing module, and the weight sensing module The system senses the weight of the item to be recycled and generates a weight sensing signal which is transmitted to the processing module. The intelligent bottle type recycling identification method as described in claim 1, wherein the recycling machine further includes a metal sensing module, the metal sensing module is electrically connected to the processing module, and the metal sensing module The system senses the item to be recycled and generates a metal sensing signal which is transmitted to the processing module.

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