Process for characterizing waste dumped in a pit and associated characterization system

Abstract

Method for characterizing waste dumped in a pit and associated characterization system. The present invention relates to a method for characterizing waste dumped in a pit, comprising the following steps: - taking (110) photographs during the unloading of a waste stream; - detecting (180) recyclable waste on at least some of the photographs and, for each of these photographs, determining a first characteristic value characterizing the presence of this recyclable waste on that photograph and a second characteristic value characterizing the waste stream; - determining (190) a percentage of recyclable waste in the dumped waste stream from the sum of the first and second characteristic values. Figure for the abstract: Figure 3

Description

Title of the invention: Method for characterizing waste dumped in a pit and associated characterization system

[0001] The present invention relates to a method for characterizing waste dumped in a pit.

[0002] The present invention also relates to a characterization system implementing such a method.

[0003] More particularly, the invention lies in the field of characterization of waste dumped into pits of energy recovery units, called UVE.

[0004] As is known per se, waste providers generally declare the nature of the waste stream to be deposited in the waste-to-energy plant's pit. This ensures the traceability of incinerated waste and allows customers to be billed according to the nature of the waste stream delivered.

[0005] However, suppliers sometimes provide a flow that is at least partially different from the one declared. This can pose a number of problems for waste-to-energy plants.

[0006] In particular, the furnaces of waste-to-energy plants are frequently blocked by oversized items, resulting in unscheduled shutdowns. The risk of accidents is higher when this type of event occurs because the personnel who have to intervene do so under dangerous conditions.

[0007] In addition, waste streams are not billed in the same way depending on their nature, which can lead to a loss of revenue for waste-to-energy plants or at least a lack of traceability of inputs.

[0008] Finally, waste collectors sometimes dump significant quantities of recyclable materials, indicating a failure to comply with the source separation obligations in force since French Law No. 2015-992 of August 17, 2015, concerning the energy transition for green growth (LTECV). Therefore, waste-to-energy plants (UVEs) would like to be able to identify offending collectors in order to pass this information on to their clients, the local authorities. Local authorities are interested in this information so they can take action at the source of waste production to achieve their objectives of reducing the amount of waste produced and improving sorting performance. The waste-to-energy plants also wish to identify collectors who bring in waste that is too bulky so they can remove it before it is incinerated, and also to raise awareness and even apply penalties to these collectors.Finally, knowing the nature of the waste brought in allows not only for billing the correct price to be paid, but also for the overall lower heating value (LHV) of the incinerated waste to be assessed. Errors in declarations made by the waste bringers distort these useful analyses. in the operation of the installation. This is why waste-to-energy plants are interested in knowing the actual nature of the discharged flows.

[0009] According to methods known in the prior art, it is possible to characterize waste by artificial intelligence with cameras installed along the conveyor belt on which the waste travels.

[0010] The methods of the state of the art propose in particular to identify recyclable objects on the carpet by identifying these objects on the different images by making correlations between these images.

[0011] Other prior art methods consist of using spectroscopy methods or other optical methods to characterize the nature of the waste.

[0012] It is therefore understandable that the state of the art offers complex and difficult-to-implement techniques for tracing waste.

[0013] In particular, these methods require significant computing power and / or complex optical installations along the carpet.

[0014] Moreover, the methods of the state of the art do not make it possible to solve the aforementioned problem, namely to assess the quantity of recyclable waste in a waste stream in order to raise awareness among offending contributors.

[0015] The present invention aims to remedy these drawbacks and to propose means for evaluating the quantity of recyclable waste in a waste stream which can be implemented in a particularly simple way and without the need for significant computing power.

[0016] To this end, the invention relates to a method for characterizing waste dumped in a pit, comprising the following steps:

[0017] - taking pictures during the unloading of a waste stream;

[0018] - detection of recyclable waste on at least some of the views and for each of these views, determination of a first characteristic value characterizing the presence of these recyclable wastes on this view and of a second characteristic value characterizing the waste flow;

[0019] - determination of a rate of recyclable waste in the waste stream dumped at starting from the set of first and second characteristic values.

[0020] Equipped with these features, the invention makes it possible to determine the percentage of recyclable waste in the waste stream in a particularly simple manner. Indeed, the invention proposes to process the data view by view in a decoupled manner to detect recyclable waste on each of these views. Thus, it is not necessary to track this recyclable waste on the different views, which generally requires very high computing power.

[0021] It has been demonstrated that the rate of recyclable waste thus identified is sufficiently reliable to be communicated to the contributors.

[0022] The method according to the invention can be implemented in a particularly simple manner in UVEs because only a camera could be sufficient to ensure the necessary image capture to implement the method.

[0023] Furthermore, the images captured by the camera can be processed remotely or locally after they are taken by a computer that does not require excessive computing power. For example, a server equipped with a graphics card capable of processing several images per second can be used.

[0024] According to some embodiments, the process further includes a step of categorizing the presence of recyclable waste in the waste stream according to a predetermined class, chosen according to the rate of this recyclable waste in this waste stream.

[0025] Thanks to these characteristics, the recyclable waste rate can be categorized according to one of the predetermined classes. The number of these classes can vary and can, for example, be between 2 and 10. Advantageously, the number of predetermined classes is equal to 2 or 4.

[0026] Categorizing the recyclable waste rate allows for a more general classification of the quantity of recyclable waste within a waste stream. As demonstrated, this rate categorization provides more valuable information to waste producers than the exact recyclable waste rate. Indeed, the latter is not always necessary to address the producers' concerns and may present greater uncertainty compared to the rate categorization. Thus, the reliability of the process can be particularly high, even with minimal implementation resources.

[0027] In some embodiments, the determination of the rate of recyclable waste includes the determination of a first sum corresponding to the sum of the first characteristic values ​​and a second sum corresponding to the sum of the second characteristic values.

[0028] Thanks to these characteristics, the rate of recyclable waste can be defined simply as a ratio between the first sum and the second sum.

[0029] According to some embodiments, the detection of recyclable waste on a corresponding view includes the detection on that view of at least one recyclable waste item and the delimitation of the item or each recyclable waste item by a shape.

[0030] Thanks to these characteristics, the detection of recyclable objects can be implemented in a simple way by using, for example, one of the techniques already available in art, such as a supervised learning technique.

[0031] The shape delimiting each recyclable object may include, for example, a simple shape such as a rectangle. In some cases, this shape may be more complex and reproduce, for example, the contours of the detected recyclable waste.

[0032] In some embodiments, the first characteristic value determined on a corresponding view is the sum of the areas of the shapes delimiting the recyclable waste on that view.

[0033] Thanks to these features, the first characteristic value is calculated simply by summing the areas of the shapes delimiting the recyclable waste. This feature is particularly advantageous when the camera is installed opposite the waste unloading location.

[0034] In certain embodiments, each second characteristic value presents an area of ​​the waste flow on the corresponding view or a generalized area of ​​the waste flow determined from the views preceding the corresponding view or from the set of views, Generalized Faire corresponding to one of the elements chosen from the group comprising:

[0035] - the sum of the areas of the waste flow over the corresponding views;

[0036] - the average of the waste flow areas on the corresponding views;

[0037] - the maximum value of the waste flow areas on the corresponding views;

[0038] - the median value of the waste flow areas on the corresponding views.

[0039] The second characteristic value can be calculated simply by calculating the areas of shapes delimiting the waste stream. Such a shape can include, for example, one or more rectangular shapes formed during the unloading of the waste stream from a truck.

[0040] In certain examples, the second characteristic value representing the area of ​​the waste stream on the corresponding view is advantageous when little recyclable waste is detected on that view. Thus, the recyclable waste stream can be delimited efficiently and its area can therefore be calculated accurately.

[0041] The second characteristic value exhibiting an averaged area or any other generalized area may be advantageous when a large number of recyclable waste items are detected on a given view.

[0042] In such a case, it can be difficult to estimate the area of ​​the waste stream, and estimates of the recyclable waste areas can then bias the calculation. In such a case, the averaged area calculated, for example, on a predetermined number of previous views or on all the views taken during unloading, provides more reliable information.

[0043] According to certain embodiments, the method further comprises a step for detecting the start and / or end of an unloading operation, the start of the unloading being detected following the detection of a predetermined minimum number of unloading objects and the end of unloading being detected following the absence of detection of a minimum number of unloading objects.

[0044] Thanks to these features, it is possible to automatically detect the start or end of unloading. Thus, no human intervention is required to initiate the estimation of the recyclable waste rate at the beginning of unloading and then stop this estimation.

[0045] In some embodiments, the process further includes a step of isolating a waste stream in the case of several discharges.

[0046] Thanks to these characteristics, it is possible to distinguish several waste streams when, for example, several unloadings take place in parallel.

[0047] Thus, it is possible to associate the unloading rate calculated by the process with the feeder corresponding to the waste stream.

[0048] In some embodiments, the process further includes determining the waste category for each recyclable waste item, with a rate of recyclable waste then being calculated for each category of recyclable waste.

[0049] Thanks to these characteristics, it is possible not only to determine the overall rate of recyclable waste but also the rate of this waste by category. Thus, the final analysis given to the supplier can be refined by specifying the rate for each waste category.

[0050] For the purposes of the invention, recyclable, as defined by ISO 14021, means the characteristic of products, packaging or associated components which can be taken from the waste stream by available processes and programs, and which can be collected, treated and put back into use in the form of raw materials or products.

[0051] Advantageously, each category of recyclable waste is chosen from a predetermined group including, for example, cardboard, plastic, and wood. However, the invention is not limited to this list given by way of example and may include any other recyclable material or object.

[0052] In some embodiments, the method further includes a step of detecting an unwanted object and warning a user following this detection.

[0053] Thanks to these features, it is possible to warn a user, for example an operator in charge of unloading control, when a bulky object is dumped into the pit. In this case, it is possible to remove this object from the pit with, for example, a grapple to prevent subsequent blockages of the furnaces.

[0054] The invention also relates to a computer program comprising software instructions which, when executed by a computer, implement the process as defined above.

[0055] The present invention also relates to a system for characterizing waste dumped in a pit, comprising technical means configured to implement the process as defined above.

[0056] The invention will be better understood upon reading the following description, given solely by way of non-limiting example and with reference to the accompanying drawings in which: - [Fig.1] [Fig.1] is a schematic view of an unloading installation; - [Fig.2] [Fig.2] is a schematic view of a characterization system according to the invention, the characterization system being connected to the unloading installation of [Fig.1] by a computer network; - [Fig.3] [Fig.3] is a flowchart of a characterization process according to the invention, the characterization process being implemented by the characterization system of [Fig.2]; - [Fig.4] [Fig.5] Figures 4 and 5 are different views illustrating an implementation of the characterization process of [Fig.3].

[0057] Fig. 1 illustrates an unloading installation 10 present for example in an energy recovery unit, also called an ERU.

[0058] With reference to this [Fig.1], the unloading installation 10 includes a pit 12, a location for unloading 13 and shooting means 14.

[0059] Pit 12, for example, is known in itself and is suitable for receiving waste. Pit 12 may, for example, have an elongated shape along a pit axis X perpendicular to the plane of [Fig. 1]. Advantageously, pit 12 is suitable for receiving waste of all kinds, including non-recyclable waste.

[0060] Thus, for example, this pit 12 can be connected by a conduit to one or more furnace(s) of the energy recovery unit.

[0061] The unloading location 13 has one or more unloading places suitable for unloading waste into the pit 12.

[0062] For example, each unloading area is adapted to receive a truck 16 or any other means of transport capable of carrying the waste. This area is then adapted so that a stream of waste 17 can fall directly into the pit 12 during its unloading. In some cases, a ramp may be provided to guide the stream of waste 17 during its unloading into the pit 12.

[0063] In the case of several unloading places, these places are for example arranged parallel to each other along the axis of pit X.

[0064] The camera-taking means 14 have one or more camera(s) arranged opposite the unloading location 13 to allow for the taking of images of each waste stream 17 during its unloading into the pit 12.

[0065] For example, the camera-capture means 14 have a camera positioned opposite each unloading bay. In other words, these cameras can be positioned along the axis of pit X.

[0066] By view, we mean each image taken by the camera(s) in isolation or each image from a series of images (i.e. video) taken by these cameras.

[0067] The image capture means 14 allow the images taken to be transmitted to a categorization system 20 via, for example, a computer network 19.

[0068] The computer network 19 can include any network known in itself. Thus, this network 19 can form a local network connecting the image capture means 14 to the characterization system 20 locally or a global computer network (such as Intermet for example) which allows the image capture means 14 to be connected to the characterization system 20 located remotely.

[0069] Alternatively, the shooting means 14 can be connected to the characterization system 20 by any other technically possible means such as, for example, cables.

[0070] The characterization system 20 is illustrated in more detail in [Fig.2].

[0071] Thus, with reference to this [Fig.2], the characterization system 20 comprises an input module 21, a processing module 22, a detection module 24 and an output module 23.

[0072] Each of these modules 21 to 24 is implemented, for example, at least partially in the form of software.

[0073] In such a case, the characterization system 20 further includes a memory for storing such software and a processor for executing this software.

[0074] Alternatively or in addition, at least one of these modules 21 to 24 presents at least partially a programmable logic circuit such as an FPGA (Field-Programmable Gate Array).

[0075] The characterization system 20 thus forms, for example, a server consisting of one or more computer(s). This characterization system 20 can thus be located, for example, locally in the corresponding energy recovery unit or remotely from it.

[0076] The input module 21 allows external data to be received and transmitted to the processing module 22 and the detection module 24.

[0077] In particular, the input module 21 is connected via the computer network 19 to the image capture means 14 to receive the views taken by these means 14. The input module 21 is further connected to a database 25 providing an object recognition model which will be explained in more detail later.

[0078] The processing module 22 allows the data received by the input module 21 to be processed in order to determine a rate of recyclable waste in a waste stream and possibly to categorize this rate into a predetermined class.

[0079] Recyclable waste is defined as any waste whose recycling is beneficial. Each recyclable waste item can, for example, be classified into a predetermined category of recyclable waste, such as cardboard, wood, and plastics. Each category can, for example, be determined by applicable legislation.

[0080] The detection module 24 enables the detection of objects on the views provided by the shooting means 14 using the object recognition model, as will be explained in more detail later.

[0081] The output module 23 allows the recyclable waste rate determined by the processing module 22 or the categorization of this rate according to a predetermined class to be transmitted to any interested external system.

[0082] Thus, for example, the processing module 23 can transmit this information in the form of a report 30 delivered to the waste provider.

[0083] According to some embodiments, the output module 23 is also adapted to communicate with a communication interface 32 accessible for example to an operator operating the unloading installation 10.

[0084] In particular, the output module 23 allows alerts to be transmitted to this interface 32, as will be explained in more detail later.

[0085] The characterization system 20 allows for the implementation of a process for characterizing waste dumped into pit 12, which will henceforth be explained with reference to [Fig.3] showing a flowchart of its steps.

[0086] During a step 110 implemented for example throughout the operation of the unloading installation 10, the input module 21 receives, advantageously in real time, the views taken by the image-taking means 14.

[0087] These views are for example taken continuously by the camera or each camera forming part of the shooting means 14.

[0088] In the next step 120, the input module 21 transmits these views to the detection module 24 which then implements the object recognition model.

[0089] As explained previously, the object recognition model, for example, is derived from database 25 and allows the detection of objects of different types using a supervised or unsupervised learning algorithm. This learning algorithm has been previously trained to recognize these objects.

[0090] In particular, the learning algorithm has been previously trained to recognize a waste stream and recyclable waste within that waste stream. In some cases, the learning algorithm has also been previously trained to categorize each recyclable waste item into one of the predetermined recyclable waste categories.

[0091] Of course, the use of other object recognition algorithms is also possible.

[0092] When the detection module 24 detects during this step 120 a minimum number of objects dumped into the pit 12 from at least one of the unloading places within a predetermined time window, the detection module 24 concludes that there is an unloading in progress during step 130. The start of unloading is therefore detected.

[0093] In certain embodiments, a spatial position filter is used to detect the start or end of unloading. This filter eliminates static detections corresponding, for example, to objects already present in the pit that could distort the detection.

[0094] Otherwise, when no spilled object is detected in any of the locations of If unloading or the number of detections is not satisfactory, the detection module 24 concludes that there is no unloading in progress and then continues to process the images acquired by the input module 21 during step 110.

[0095] During step 140, the detection module 24 stores the views of the unloading in progress, for example in a RAM of the characterization system 20. This step 140 is implemented as long as the detection module 24 detects an unloading in progress during step 130.

[0096] When the detection module 24 no longer detects a sufficient number of objects, for example within a predetermined time window, the detection module 24 concludes that there is no more unloading in progress. The end of the unloading is then detected, and the processing module 22 proceeds to execute the following steps 160 to 200, which form a post-processing (PP) phase of the views stored during step 140.

[0097] Alternatively, the start and end of unloading are detected by any other means (using, for example, presence sensors) or are then communicated manually, for example, by the operator.

[0098] In some embodiments, the process may further include at least one step implemented on the fly, for example during the execution of steps 130 and 140.

[0099] Thus, for example, the method may include a step 150 in which the detection module 24 analyzes each object detected in step 130 and, when it is a bulky object, i.e., an object whose dimensions exceed predetermined thresholds, issues an alert to the operator. In such a case, this alert is transmitted by the output module 23 to the corresponding interface 32. Alternatively, step 150 is one of the steps implemented during the PP post-processing phase.

[0100] In such a case, the warning given to the operator is then subsequent to the unloading.

[0101] In addition, in some embodiments, steps 160 to 200 explained below can also be implemented on the fly, for example during the execution of steps 130, 140.

[0102] In the following description, these steps 160 to 200 will be considered to belong to the post-processing phase PP.

[0103] During an initial step 160 of the post-treatment phase PP, the processing module 22 first isolates the views relating to the same waste stream when several unloadings have been carried out in parallel.

[0104] For this, various techniques are possible.

[0105] For example, when a camera is dedicated to each of the unloading places, the processing module 22 isolates the views taken by the same camera and thus considers that all these views are related to the same waste stream.

[0106] When this is not possible, or when the views taken by at least some of the cameras contain several waste streams in the same view, the processing module 22 delimits each grouping of objects in the views. This delimitation can, for example, be done using a predetermined shape such as a rectangle.

[0107] When the shapes are sufficiently far apart, the processing module 22 concludes that they are different waste streams.

[0108] Otherwise, the processing module 22 concludes that the two groupings are part of the same flow and unites these groupings by the same form.

[0109] According to another embodiment, it is possible to apply a statistical method to determine that a set of points corresponds to a mixture of two distributions. This can be done, for example, by clustering. Each of these distributions therefore corresponds to a waste stream.

[0110] It is considered that the following steps 170 to 200 are implemented for each waste stream identified by the treatment module 22.

[0111] In particular, during step 170 which may be in certain embodiments Optionally, the processing module 22 identifies the source of the corresponding waste stream.

[0112] This can be done for example by identifying on the corresponding views a registration plate of the means of transport 16 which brought back this flow of waste.

[0113] Alternatively, identification may be carried out using any other technically feasible means.

[0114] During the next step 180, the processing module 22 processes view by view to detect recyclable waste on these views.

[0115] According to one embodiment, to do this, the processing module 22 processes each view acquired by the image capture means 14 in relation to the corresponding waste stream.

[0116] According to another embodiment, the processing module 22 processes only certain views taken by the shooting means 14. These views to be processed are, for example, taken from the set of views taken by these shooting means 14 at a predetermined frequency.

[0117] To detect recyclable waste, the processing module 22 uses the object recognition model explained previously.

[0118] In one possible embodiment, when the processing module 22 detects no recyclable waste in a given view, the processing module 22 rejects that view from any further analysis. Conversely, when the processing module 22 detects at least one recyclable waste item in a given view, the processing module 22 first determines a primary characteristic value indicating the presence of recyclable waste in that view. In another embodiment, all views are retained for future analysis.

[0119] Advantageously, this first characteristic value corresponds to the sum of the areas of all recyclable waste detected on the corresponding view.

[0120] More specifically, to achieve this, the processing module 22 delimits each recyclable waste item on the given view by a predetermined shape. This shape may, for example, correspond to a rectangle. Then, the processing module 22 determines the area of ​​this shape and then the sum of all the determined areas. Optionally, in some examples, these areas may be normalized.

[0121] Then, the processing module 22 determines a second characteristic value characterizing the waste flow on the corresponding view.

[0122] As in the case of the first characteristic value, the second characteristic value can describe an area of ​​the corresponding waste stream.

[0123] This second characteristic value can be determined differently depending on the case.

[0124] In particular, when relatively little recyclable waste is detected on the corresponding view, the processing module 22 can then delimit the waste stream by a predetermined shape such as a rectangle and then determine the area of ​​this shape.

[0125] When, on the contrary, a large number of recyclable waste items are detected on the corresponding view, making it impossible or difficult to determine the area from the shape delimiting the corresponding waste stream, the treatment module 22 determines an averaged area of ​​the waste stream.

[0126] For its part, this averaged area can also be determined according to different possibilities.

[0127] According to a first possibility, this averaged area presents a moving averaged area over, for example, a predetermined number of consecutive views processed previously.

[0128] According to a second possibility, this averaged area is calculated from a predetermined number of views relating to this waste stream or from all views of this stream.

[0129] In particular, in the latter case, the second characteristic value can only be determined once for all views.

[0130] According to another embodiment, a generalized area is calculated from the corresponding views. In addition to the averaged area, this generalized area may correspond to:

[0131] - the sum of the areas of the waste flow over the corresponding views;

[0132] - the maximum value of the waste flow areas on the corresponding views;

[0133] - the median value of the waste flow areas on the corresponding views.

[0134] In some embodiments, each first and / or each second characteristic value is / are determined with a confidence interval allowing the rate of recyclable waste in the stream to be framed.

[0135] An example of the implementation of this step 180 is illustrated in Figures 4 and 5.

[0136] Thus, this [Fig.4] represents five consecutive views processed by the processing module 22 of the same waste stream.

[0137] In this example, no recyclable waste was detected in views 1, 3 and 5. Therefore, these views are excluded from any future consideration.

[0138] With regard to views 2 and 4, it is assumed that the second characteristic value is the same for both views and is determined, for example, by averaging the area of ​​the waste stream detected over the different views. This average value is equal, for example, to 5. Alternatively, at least in view 2, the second characteristic value V2 is determined from that same view as the area of ​​the shape delimiting the stream.

[0139] Moreover, the first characteristic value V1 determined in relation to view 2 is equal to 3 and this value VI determined in relation to [Fig.4] is equal to 1.

[0140] In the example of [Fig.5], no recyclable waste is detected on view 1. On the other hand, on views 2, 3 and 4, a large number of recyclable wastes have been detected so that the first characteristic value V1 on view 2 is equal to 4, on view 3 is equal to 3 and on view 4 is equal to 1.

[0141] On the other hand, for each of these views, the second characteristic value V2 is taken as a moving average value which is equal in the example of [Fig.5] to 5 in relation to each of the views 2, 3 and 4.

[0142] Many other examples are also possible.

[0143] In particular, when, for example, only recyclable waste is present in the first view processed by the processing module 22 (for example, a large cardboard box), the second characteristic value can be equal to the first characteristic value. For all consecutive views, an averaged value can then be used.

[0144] In the next step 190, the processing module 22 determines a rate of recyclable waste in the discharged waste stream using the first and second characteristic values ​​determined in the previous step.

[0145] In particular, in this step, the processing module 22 calculates this rate as a ratio between the sum of the first characteristic values ​​and the sum of the second characteristic values.

[0146] In the next step 200, the processing module 22 categorizes the recyclable waste rate calculated in the previous step into a predetermined class. To do this, the processing module 22 compares this rate with several thresholds defining these predetermined classes and then categorizes the rate determined as a result of this comparison into a class. According to one embodiment, four classes are determined: class 1 non-existent, class 2 low, class 3 high, and class 4 significant. According to another example, two classes are determined, namely class 1 non-existent and class 2 high.

[0147] Thus, for example, when the recyclable waste rate is less than 10%, it is classified in class 1 (non-existent). When this rate is between 10 and 50%, it is classified in class 2 (low). When this rate is between 50 and 90%, it is classified in class 3 (significant). Finally, when this rate is greater than 90%, it is classified in class 4 (significant).

[0148] Of course, other classifications are still possible.

[0149] It should also be noted that, during step 180, it is also possible to associate each recyclable waste item with a category of that waste. Each category is, for example, chosen from the group comprising: cardboard, plastic, and wood.

[0150] Thus, in such a case, it is possible to determine the first characteristic value for each type of recyclable waste. Therefore, in such a case, during step 190, a rate for each category of recyclable waste can then be calculated.

[0151] Similarly, in step 200, each rate is categorized into the corresponding class.

Claims

Demands

1. A method for characterizing waste dumped in a pit (12), comprising the following steps: - taking (110) views during the unloading of a waste stream (17); - detecting (180) recyclable waste on at least some of the views and for each of these views, determining a first characteristic value characterizing the presence of this recyclable waste on this view and a second characteristic value characterizing the waste stream (17); - determining (190) a rate of recyclable waste in the waste stream (17) dumped from the set of the first and second characteristic values.

2. A method according to claim 1, further comprising a step (200) of categorizing the presence of recyclable waste in the waste stream (17) according to a predetermined class, chosen according to the rate of such recyclable waste in this waste stream (17).

3. A method according to claim 1 or 2, wherein the determination of the rate of recyclable waste includes the determination of a first sum corresponding to the sum of the first characteristic values ​​and a second sum corresponding to the sum of the second characteristic values.

4. A method according to any one of the preceding claims, wherein the detection of recyclable waste on a corresponding view includes the detection on that view of at least one recyclable waste item and the delimitation of the item or items of recyclable waste by a shape.

5. A method according to claim 4, wherein the first characteristic value determined on a corresponding view is the sum of the areas of the shapes delimiting the recyclable waste on that view.

6. A method according to any one of the preceding claims, wherein each second characteristic value presents an area of ​​the waste stream (17) on the corresponding view or a generalized area of ​​the waste stream (17) determined from the views preceding the corresponding view or from all the views, the generalized area corresponding to one of the elements selected from the group comprising: - the sum of the waste flow areas on the corresponding views; - the average of the waste flow areas on the corresponding views; - the maximum value of the waste flow areas on the corresponding views; - the median value of the waste flow areas on the corresponding views.

7. A method according to any one of the preceding claims, further comprising a step of detecting the start of an unloading and / or the end of such unloading, the start of the unloading being detected following the detection of a predetermined minimum number of unloading objects and the end of the unloading being detected following the absence of detection of a minimum number of unloading objects.

8. A method according to any one of the preceding claims, further comprising an isolation step (160) of a waste stream (17) in the case of multiple discharges.

9. A method according to any one of the preceding claims, further comprising determining for each recyclable waste the waste category, a rate of recyclable waste then being calculated for each category of recyclable waste.

10. A method according to any one of the preceding claims, further comprising a step (150) of detecting an unwanted object and warning a user following such detection.

11. A computer program comprising software instructions which, when executed by a computer, implement the method according to any one of the preceding claims.

12. System for characterizing (20) waste dumped into a pit (12), comprising technical means (21, 22, 23, 24) configured to implement the process according to any one of claims 1 to 10.

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