Method for processing blockages of a vehicle camera lens and system for detecting a blockage of the lens

Abstract

A method for processing lens blockages of a vehicle camera comprises receiving an image from a vehicle camera, generating a first camera lens contamination map according to the pixel texture richness and color spectrum levels extracted from the image, receiving a sequential input of multiple frames from the vehicle camera, generating a second camera lens contamination map according to at least one conspicuous area extracted from the multiple frames, combining the first camera lens contamination map and the second camera lens contamination map to produce a combined camera lens contamination map, and initiating an automatic lens cleaning and / or a camera lens blocking signal according to the combined camera lens contamination map.

Description

introduction

[0001] The present disclosure relates generally to an obstruction or blockage of an objective or lens of a vehicle camera, including a detection of a blockage of the lens of the vehicle camera in order to control the automatic lens cleaning and other vehicle control features.

[0002] Document US 2023 / 0068848A1 discloses a method for identifying and classifying a contaminant in a field of view of an imaging device.

[0003] Some vehicles are equipped with cameras, such as reversing cameras, that provide the driver with a live image when backing up. Other vehicle systems can use camera images to perceive the surroundings and control various vehicle functions, such as providing audible warnings when an object is detected close to the rear of the vehicle. Sometimes, dirt or other debris can accumulate on a vehicle camera lens, negatively impacting systems that rely on accurate images from the camera. Summary

[0004] A method for processing lens blockages of a vehicle camera comprises receiving an image from a vehicle camera, generating a first camera lens contamination map according to pixel texture richness and color spectrum levels extracted from the image, receiving a sequential input of several individual images or frames from the vehicle camera, generating a second camera lens contamination map according to at least one conspicuous area extracted from the several frames, combining the first camera lens contamination map and the second camera lens contamination map to generate a combined camera lens contamination map, and initiating an automatic lens cleaning and / or a camera lens blocking signal according to the combined camera lens contamination map.

[0005] Other features include initiating automatic lens cleaning by dispensing a cleaning solution onto a lens of the vehicle camera and / or moving a windshield wiper across a surface of the lens.

[0006] Other features include initiating the camera lens blocking signal, which includes warning at least one vehicle motion control system of a blocking of a vehicle camera lens in order to prevent the generation of automatic vehicle braking and / or audible warnings in accordance with the input received from the vehicle camera.

[0007] In other features, the method includes extracting texture sub-areas of the image, discretizing an image blur level according to the pixel texture richness of the texture sub-areas, and extracting color spectrum sub-areas of the image, wherein the first camera lens contamination map is generated by combining the image blur level and the color spectrum sub-areas.

[0008] In other features, the method includes extracting and tracking the at least one conspicuous area in the multiple frames and detecting at least one blockage area of ​​the vehicle camera corresponding to the at least one conspicuous area, whereby the second camera lens contamination map is generated according to the at least one blockage area.

[0009] In other features, the method includes receiving at least one vehicle motion signal and generating a timeline of pixel statistics in the multiple frames according to the at least one vehicle motion signal, wherein the at least one striking area is tracked according to the timeline of pixel statistics in the multiple frames and wherein at least one blocking area is detected according to the timeline of pixel statistics in the multiple frames.

[0010] In other features, tracking the at least one conspicuous area includes identifying a point in the multiple frames where detected objects repeatedly disappear during vehicle movement.

[0011] In other features, at least one vehicle motion signal includes a steering wheel rotation signal and / or a wheel speed sensor signal. In other features, combining the first camera lens pollution map and the second camera lens pollution map includes applying a weighted average to the first camera lens pollution map and the second camera lens pollution map to generate the combined camera lens pollution map.

[0012] In other features, image acquisition includes obtaining the image from an unprocessed camera feed (raw camera feed) of the vehicle camera and includes generating the initial camera lens contamination map, generating an information map based on the image from the raw camera feed, and determining a valid perception area based on the information map. In other features, the vehicle camera includes a reversing camera for the vehicle.

[0013] A system for detecting lens blockage in a vehicle camera comprises a vehicle camera mounted on a vehicle, wherein the vehicle camera includes a lens and is configured to capture images, and a vehicle control module in communication link with the vehicle camera, wherein the vehicle control module is configured to receive an image from the vehicle camera, extract texture sub-areas of the image, discretize an image blur level according to the pixel texture richness of the texture sub-areas, extract color spectrum sub-areas of the image, generate a camera lens contamination map according to the pixel texture richness of the texture sub-areas and the color spectrum sub-areas of the image, and initiate automatic lens cleaning and / or a camera lens blockage signal according to the camera lens contamination map.

[0014] In other features, initiating automatic lens cleaning includes dispensing a cleaning solution to a lens of the vehicle camera and / or moving a windshield wiper over a surface of the lens, and initiating the camera lens blocking signal includes warning at least one vehicle motion control system of a blockage of a lens of the vehicle camera in order to prevent the generation of automatic vehicle braking and / or an audible warning signal in accordance with the input received from the vehicle camera.

[0015] Further applications of the present disclosure will become apparent from the detailed description, the claims, and the drawings. The detailed description and the specific examples are intended for illustrative purposes only. Brief description of the drawings

[0016] The present disclosure is more fully understood from the detailed description and the accompanying drawings, whereby: Fig. 1 is a representation of an exemplary vehicle that includes a vehicle control module configured to detect an obstruction or blockage of a vehicle camera lens; Fig. 2 is a flowchart that illustrates an exemplary process for detecting a blockage of a lens in a vehicle camera; Fig. 3 is a flowchart that illustrates an exemplary process for detecting a blockage of a lens according to a single camera image; Fig. 4 is a flowchart that illustrates an exemplary process for detecting a blockage of a lens according to a sequential input of several frames from a vehicle camera; Fig. 5A and Fig. 5B Exemplary illustrations of an image information map and a valid perception area for a clear or unobstructed lens of a reversing camera are; Fig. 6A and Fig. 6B are exemplary illustrations of an image information map and a valid perception area for a completely blocked lens of a reversing camera; Fig. 7A and Fig. 7B Exemplary illustrations of an image information map and a valid perception area for a partially blocked lens of a reversing camera are; and Fig. 8A and Fig. Figure 8B shows exemplary illustrations of a free lens of a reversing camera and a partially blocked lens of a reversing camera.

[0017] Reference numbers can be reused in the drawings to identify similar and / or identical elements. Detailed description

[0018] Vehicle cameras are sometimes blocked by dirt, water, or other contaminants. Some exemplary embodiments described herein implement a simple computational method to detect blocked areas or zones of a vehicle camera lens and to provide blockage detection signals to support vehicle camera vision functions, cleaning functions, perception features, etc. The blocked zones may include blockages of at least part of the vehicle camera lens due to dirt, water, snow, ice, mud particles, etc., which may be generally referred to as lens contamination zones or lens contamination maps.

[0019] An exemplary detection algorithm comprises two components for detecting blocked zones or areas of the vehicle lens, although other exemplary embodiments may use each component individually or combine the component(s) with other detection features. For example, with a stationary vehicle (or when a single camera image is obtained), the texture richness and color spectrum levels can be extracted from the camera image and used to estimate lens blockage in a vehicle camera.

[0020] In various implementations, the depth of field of a vehicle camera can be greater than zero (e.g., approximately 20 centimeters, etc.), and typical natural scenes of the ground and vehicle environment are rich in texture. Therefore, a vehicle control module can be configured to detect areas in a captured camera image with a high degree of blur and lack of color as blocked zones or areas of the vehicle camera lens.

[0021] In some exemplary embodiments, a vehicle control module can be configured to estimate camera lens obstructions while the vehicle is in motion, based on tracking conspicuous areas across multiple camera frames in combination with vehicle motion signals. For example, if the vehicle motion signals indicate known vehicle movement, a vehicle control module can track conspicuous areas in the multiple camera frames and / or track conspicuous areas between multiple cameras as strong evidence of a camera lens obstruction (e.g., obstruction by dirt).

[0022] Referring to Fig. 1 comprises a vehicle with 10 front wheels (12) and 13 rear wheels. Fig. 1 A drive unit 14 selectively transmits torque to the front wheels 12 and / or the rear wheels 13 via drive lines 16 and 18, respectively. The vehicle 10 can contain various types of drive units. For example, the vehicle can be an electric vehicle such as a battery electric vehicle (BEV), a hybrid vehicle or a fuel cell vehicle, a vehicle containing an internal combustion engine (ICE), or another type of vehicle.

[0023] Some examples of the drive unit 14 can include any suitable electric motor, an inverter or power inverter, and a motor controller configured to control circuit breakers within the power inverter to set or adjust the motor speed and torque during operation and / or regeneration. A battery system supplies current or power to the electric motor of the drive unit 14 via the power inverter during operation or receives it from it during regeneration.

[0024] While the vehicle 10 in Fig. While the vehicle has one drive unit 14, it can have 10 other configurations. For example, two separate drive units can power the front wheels 12 and the rear wheels 13, one or more individual drive units can power individual wheels, etc. As can be understood, other vehicle configurations and / or drive units can be used.

[0025] The vehicle control module 20 can be configured to control the operation of one or more vehicle components, such as the drive unit 14 (e.g., by instructing or commanding torque settings of an electric motor of the drive unit 14). The vehicle control module 20 can receive inputs for controlling vehicle components, such as signals received from a steering wheel, accelerator pedal, brake pedal, etc. The vehicle control module 20 can monitor the vehicle's telematics for safety purposes, such as vehicle speed, vehicle location, vehicle braking and acceleration, etc.

[0026] The vehicle control module 20 can receive signals from any suitable components to monitor one or more aspects of the vehicle, including one or more vehicle sensors (such as cameras, microphones, pressure sensors, wheel position sensors, location or position sensors such as antennas of a global positioning system (GPS), etc.). Some sensors can be configured to monitor the vehicle's current motion, acceleration, steering torque, etc.

[0027] As in Fig. As shown in Figure 1, the vehicle 10 comprises a rear camera 24, an optional front camera 26, and an optional side camera 28. In different implementations, the vehicle 10 may contain more or fewer (or none) of these cameras. Each camera can include any suitable camera lens and image acquisition components and may optionally be connected to a laser, lidar sensor, etc.

[0028] The vehicle cameras can be used to detect objects in the vicinity of the vehicle 10. In some exemplary embodiments, the vehicle cameras can be configured to detect a nearby vehicle in the path (e.g., another vehicle ahead of the current path of the vehicle 10) or a vulnerable road user (VRU) (e.g., a pedestrian or cyclist), and can be implemented as reversing cameras to control automatic braking or audible warnings while the vehicle is reversing.

[0029] The data acquired by the vehicle cameras can be used to control features for the automatic control of vehicle movement, such as automatic acceleration and deceleration based on detected objects or vehicles in the vicinity of the vehicle 10. In some situations, a lens of the vehicle camera may be blocked (by dirt, water, snow, ice, etc.), which could negatively affect the vehicle control functions if the vehicle control module 20 cannot receive clear images from the vehicle camera.

[0030] In various embodiments, an automatic lens cleaning system can be controlled via a signal from the vehicle control module 20. For example, in response to a control signal from the vehicle control module 20, a cleaning solution can be dispensed or sprayed onto the camera lens; a windshield wiper or other component can move across a surface of the lens in response to a control signal; a heating device can be activated to melt snow or ice on the lens, etc.

[0031] The vehicle control module 20 can communicate with another device or apparatus via a wireless communication interface, which may include one or more wireless antennas for transmitting and / or receiving wireless communication signals. For example, the wireless communication interface can use any suitable wireless communication protocol, including but not limited to vehicle-to-everything (V2X) communication, Wi-Fi communication, wireless area network (WAN) communication, cellular communication, personal area network (PAN) communication, short-range wireless communication (e.g., Bluetooth), etc.The interface for wireless communication can communicate with a remote computer device via one or more wireless and / or wired networks. With regard to vehicle-to-vehicle (V2X) communication, the vehicle can include one or more V2X transceivers (e.g., V2X signal transmitting and / or receiving antennas).

[0032] The vehicle 10 also includes a user interface. The user interface can include any suitable displays (e.g., on a dashboard, console, or elsewhere), a touchscreen or other input devices, speakers for generating audio signals, etc. In some exemplary embodiments, the vehicle control module 20 can be configured to provide a visual notification of a recommended evasive steering maneuver on the user interface and / or generate an audible warning of the recommended evasive steering maneuver.

[0033] Fig. Figure 2 is a flowchart illustrating an exemplary process for detecting a lens blockage in a vehicle camera. This process can be implemented, for example, using the vehicle control module 20. Fig. 1. The process begins at 204 by taking an image from a vehicle camera, such as an image from the vehicle's reversing camera 24. Fig. 1. A captured image is obtained. In some exemplary embodiments, the image can be a single static image or frame captured by the vehicle camera.

[0034] In version 208, the vehicle control module is configured to generate an initial camera lens contamination map based on extracted image features. For example, one or more features can be extracted from the image, such as blur levels, areas with pixel texture richness (e.g., sub-areas of the image), color spectrum levels (e.g., sub-areas with different color spectra), etc. (e.g., analyzed, determined, obtained, etc.).

[0035] Further details on creating the first camera lens contamination map are given below with reference to Fig. 3 described. The camera lens contamination map can generally refer to a map of predicted areas of camera lens blockage that may be due to particles of dirt, mud, water, snow, ice, etc.

[0036] In version 212, the vehicle control module is configured to receive a sequential input of multiple individual images or frames from the vehicle camera. For example, the vehicle control module can receive a video from the vehicle's rear or reversing camera 24, containing several consecutive or sequential frames. These multiple sequential frames can comprise any suitable number of frames (such as at least ten frames, at least 100 frames, etc.) or can comprise a number of frames for a fixed period (such as one second of frames, ten seconds of frames, one minute of frames, etc.). The frames can be selected at regular or periodic intervals, such as one every five frames received from the vehicle camera, one every twenty frames received from the vehicle camera, etc.

[0037] At 216, the controller is configured to generate a second camera lens pollution map based on the features extracted from the multiple frames. Additional details on generating the second camera lens pollution map based on the features extracted from the multiple frames are provided below with reference to Fig. 4 described.

[0038] The vehicle control module is configured to combine the first camera lens contamination map and the second camera lens contamination map at 220. This allows for accurate lens contamination detection by combining an analysis of features extracted from a single static image with features determined from a sequence of frames (e.g., a video) to analyze the movement of objects across the frames.

[0039] In other exemplary embodiments, lens blocking using only a single static image (e.g., as in Fig. 3 described and illustrated) or can lens blockage be detected using only an analysis of the sequence of several frames (e.g. as in Fig. 4 described and illustrated) can be detected.

[0040] At 224, the vehicle control module is configured to determine whether the combined map indicates a lens obstruction. For example, if at least one blocked area is identified (e.g., due to a blocked or invalid perceptual area in the processed image map data), the vehicle control module can determine that at least part of the lens is blocked. Although Fig. 2. Determining whether lens blockage is present, based on the combined map, is illustrated. In other exemplary embodiments, the determination of lens blockage may be based solely on an analysis of the single static image (e.g., as in Fig. 3 described and illustrated) or can lens blockage be determined based solely on an analysis of the sequence of several frames (e.g., as in Fig. 4 described and illustrated).

[0041] If the control unit at 224 determines that there is no lens obstruction, it returns to 204 to obtain another image from the vehicle camera. Lens obstruction checks can be performed at specific periodic intervals, such as every second, every minute, every hour, etc.

[0042] If the controller at 224 determines that the combined map indicates lens obstruction, the controller proceeds to 228 to initiate a camera lens obstruction signal. This signal can indicate to other vehicle systems that at least part of the vehicle camera image is currently obstructed, preventing those systems from reliably using the camera image for control functions. For example, a rear braking system should not rely solely on the camera image to determine whether an object is behind the vehicle in order to control automatic braking or the generation of an audible warning.

[0043] At 232, the control unit determines whether the vehicle is equipped with an automatic lens cleaning system. If so, at 236, the control unit initiates the automatic cleaning of the vehicle camera lens. For example, the vehicle control module can send a control signal to spray a cleaning solution onto the vehicle camera lens, activate a windshield wiper to wipe a surface of the vehicle camera lens, etc.

[0044] Fig. Figure 3 is a flowchart illustrating an exemplary process for detecting lens blockage based on a single camera image. This process can be implemented, for example, using the vehicle control module 20. Fig. 1. At 304, the process begins by obtaining an image from a vehicle camera.

[0045] At 308, the vehicle control module is configured to extract texture sub-regions from the image. The control unit then discretizes an image blur level corresponding to the pixel texture richness of the texture sub-regions at 312. An image texture can, for example, include one or more metrics calculated via image processing to quantify a perceived texture of an image. The pixel image texture can provide information about the spatial arrangement of color or intensity in an image or a selected area of ​​the image, such as by utilizing segmentation or classification to identify, for example, the spatial frequency, average gray value, etc.

[0046] In version 316, the vehicle control module is configured to extract color spectrum sub-areas from the image. Information about the color spectrum can be obtained, for example, by performing image processing on the image to determine the positions or locations of the color values, a range of color values ​​used in the image, color patterns, or a match within the image, etc.

[0047] The vehicle control module is configured to generate the camera lens contamination map according to pixel texture richness and color spectrum levels at 320 Hz. This camera lens contamination map can be used alone to determine if lens blockage is present, or it can be combined with a camera lens contamination map based on a sequence of images, as shown in Fig. 2 is described.

[0048] Fig. Figure 4 is a flowchart illustrating an exemplary process for detecting lens blockage based on the sequential input of multiple frames from the vehicle camera. This process can be implemented, for example, using the vehicle control module 20. Fig. 1. The process begins with error 404, where a sequential input of multiple frames is received from a vehicle camera.

[0049] In version 408, the control unit receives at least one vehicle motion signal. For example, the vehicle control module might receive a value for the steering wheel rotation, a wheel speed sensor value, etc. In version 412, the vehicle control module is configured to generate a timeline of pixel statistics across multiple frames, corresponding to a vehicle motion sensor. For example, the vehicle control module might use image processing to obtain statistics from multiple frames and correlate these statistics with detected vehicle movement at different times.

[0050] The vehicle control module is configured to extract and track at least one conspicuous area in the multiple frames when the value is 416.

[0051] For example, the vehicle control module can be configured to process multiple frames to determine an expected path for an object within those frames. If a rock is detected in a rear-facing camera while the vehicle is moving forward, the vehicle control module can expect the rock to move further away and become smaller in the image as the vehicle continues to move forward.

[0052] If the vehicle control module determines that the identified object (e.g., a rock) disappears from the frame earlier than expected, it may determine that a lens obstruction is present in the area of ​​disappearance. If multiple objects at the same location continue to disappear unexpectedly across multiple frames, the vehicle control module can more confidently assume that a lens obstruction is present at the same location.

[0053] At 420, the control unit detects at least one blocking zone or area of ​​the vehicle camera corresponding to at least one conspicuous area. For example, if the vehicle control module identifies an area of ​​the image where the tracked object unexpectedly disappears while the vehicle is moving, as mentioned above, the control unit can determine that a blocking area exists in this region.

[0054] In version 424, the vehicle control module is configured to generate a camera lens contamination map corresponding to at least one blockage area. This camera lens contamination map can be used alone to determine whether lens blockage is present, or it can be combined with a camera lens contamination map based on a single static image, as shown in Fig. 2 is described.

[0055] Fig. 5A and Fig. Figure 5B are exemplary illustrations of an image information map and a valid perception area for a clear or unobstructed reversing camera lens. As in Fig. As shown in 5A, the image information card 501 contains a normal amount of pixel detail in a free camera view.

[0056] As in Fig. As shown in Figure 5B, a corresponding valid perception area 503 indicates a large zone or area of ​​valid perception by the vehicle camera when the lens is clear. The valid perception area 503 can be determined by performing image processing on pixel data from the image information map 501.

[0057] Fig. 6A and Fig. Figure 6B shows exemplary illustrations of an image information map and a valid perception area for a completely blocked reversing camera lens. As in Fig. As shown in Figure 6A, the image information map 601 contains essentially no pixel details when the lens view is completely blocked. As shown in Fig. As shown in Figure 6B, a corresponding valid perception area 603 shows hardly any zones or areas with valid perception by the vehicle camera when the lens is completely blocked.

[0058] Fig. 7A and Fig. Figure 7B shows exemplary illustrations of an image information map and a valid perception area for a partially blocked lens of a reversing camera. As in Fig. As shown in Figure 7A, the image information map 701, when the lens view is partially blocked, contains some zones or areas with a normal amount of pixel detail and some areas with essentially no pixel detail. As shown in Fig. As shown in Figure 7B, a corresponding valid perception area 703 shows some areas with valid perception by the vehicle camera, but other areas without valid perception by the vehicle camera when the lens is partially blocked.

[0059] The vehicle control module can be configured to determine whether lens blockage is present based on information from the processed valid perception areas. For example, if valid perception area 503 is analyzed, the vehicle control module can determine that the vehicle lens is completely clear; if valid perception area 603 is analyzed, it can determine that the vehicle lens is completely blocked; and if valid perception area 703 is blocked, it can determine a location with partial lens blockage.

[0060] Fig. 8A and Fig. Figure 8B shows exemplary illustrations of a clear or unobstructed reversing camera lens and a partially blocked reversing camera lens. Fig. Figure 8A shows a valid perception range 801 when the vehicle's lens is clear. In contrast, the valid perception range 803 of Fig. 8B an identified blocking area 804. The vehicle control module can be configured to detect the lens blocking area and send a lens blocking signal to other vehicle systems that rely on images or a specific area of ​​images from the vehicle camera.

[0061] Spatial and functional relationships between elements (for example, between modules, circuit elements, semiconductor layers, etc.) are described using various terms, including "connected," "interlocked," "coupled," "adjacent," "near or beside," "on," "above," "below," and "arranged." Unless explicitly described as "direct," when a relationship between first and second elements is described in the above disclosure, this relationship may be a direct relationship, in which no other intervening elements exist between the first and second elements, or it may be an indirect relationship, in which one or more intervening elements (either spatial or functional) exist between the first and second elements.As used here, the phrase “at least one of A, B and C” should be understood as meaning a logical (A OR B OR C) using a non-exclusive logical OR, and should not be understood as meaning “at least one of A, at least one of B and at least one of C”.

[0062] In the diagrams, the direction of an arrow, as indicated by its tip, generally illustrates the flow of information (e.g., data or instructions) that is relevant to the illustration. For example, if Element A and Element B exchange a variety of information, but information transferred from Element A to Element B is important for the illustration, the arrow may point from Element A to Element B. This unidirectional arrow does not imply that no other information is transferred from Element B to Element A. Furthermore, Element B may send requests for, or acknowledgments of, information to Element A in connection with the information transferred from Element A to Element B.

[0063] In this application, including the definitions below, the term "module" or the term "controller" may be replaced by the term "circuit". The term "module" may refer to, be part of, or include an application-specific integrated circuit (ASIC); a digital, analog, or mixed analog / digital discrete circuit; a digital, analog, or mixed analog / digital integrated circuit; a combinational logic circuit; a field-programmable gate array (FPGA); a processor circuit (shared, dedicated, or group) that executes code; a memory circuit (shared, dedicated, or group) that stores code executed by the processor circuit; other suitable hardware components that provide the described functionality; or a combination of some or all of the above components, such as in a system-on-a-chip.

[0064] The module may contain one or more interface circuits. In some examples, the interface circuits may include wired or wireless interfaces connected to a local area network (LAN), the internet, a wide area network (WAN), or combinations thereof. The functionality of any given module of this disclosure may be distributed among multiple modules connected via interface circuits. For example, multiple modules may enable load balancing. In another example, a server module (also known as a remote or cloud module) may perform some functions for a client module.

[0065] The term "code," as used above, can include software, firmware, and / or microcode, and can refer to programs, routines, functions, classes, data structures, and / or objects. The term "shared processor circuit" refers to a single processor circuit that executes some or all of the code from multiple modules. The term "group processor circuit" refers to a processor circuit that, in combination with additional processor circuits, executes some or all of the code from one or more modules. References to multiple processor circuits include multiple processor circuits on separate chips, multiple processor circuits on a single chip, multiple cores of a single processor circuit, multiple threads of a single processor circuit, or a combination of the above.The term shared memory circuit refers to a single memory circuit that stores some or all of the code from multiple modules. The term group memory circuit refers to a memory circuit that, in combination with additional memory, stores some or all of the code from one or more modules.

[0066] The term storage circuit is a subset of the term computer-readable medium. The term computer-readable medium, as used here, does not include transitory electrical or electromagnetic signals that propagate through a medium (such as on a carrier wave); the term computer-readable medium can therefore be considered material and non-transient.Non-restrictive examples of a non-transient, physical, computer-readable medium include non-volatile memory circuits (such as a flash memory circuit, a erasable programmable read-only memory circuit, or a mask read-only memory circuit), volatile memory circuits (such as a static random access memory circuit or a dynamic random access memory circuit), magnetic storage media (such as an analog or digital magnetic tape or a hard disk drive), and optical storage media (such as a CD, a DVD, or a Blu-ray Disc).

[0067] The devices and methods described in this application can be partially or fully implemented by means of a special-purpose computer created by configuring a general-purpose computer to perform one or more specific functions embodied in computer programs. The functional blocks, flowchart components, and other elements described above serve as software specifications that can be translated into computer programs through the routine work of a person skilled in the art or a programmer.

[0068] Computer programs contain instructions executable by processors, stored on at least one non-transitory, physical, computer-readable medium. Computer programs may also contain or rely on stored data. Computer programs may include a basic input / output system (BIOS) that interacts with the computer's special-purpose hardware, device drivers that interact with specific devices of the computer for special purposes, one or more operating systems, user applications, background services, background applications, etc.

[0069] Computer programs can contain: (i) descriptive text to be parsed, such as HTML (Hypertext Markup Language), XML (Extensible Markup Language), or JSON (JavaScript Object Notation); (ii) assembly code; (iii) object code generated from source code by a compiler; (iv) source code for execution by an interpreter; (v) source code for compilation and execution by a just-in-time compiler; etc. For example, source code can be written using the syntax of languages ​​such as C, C++, C#, Objective-C, Swift, Haskell, Go, SQL, R, Lisp, Java®, Fortran, Perl, Pascal, Curl, OCaml, Javascript®, HTML5 (Hypertext Markup Language 5th Revision), Ada, ASP (Active Server Pages), PHP (PHP: Hypertext Preprocessor), Scala, Eiffel, Smalltalk, Erlang, Ruby, Flash®, and Visual Studio. Include Basic®, Lua, MATLAB, SIMULINK and Python®. legend

[0070] In the drawing figures, N stands for no and Y for yes.

Claims

[1] A method for processing blockages of a lens of a vehicle camera (24, 26, 28), the method comprising: Obtaining (204) an image from a vehicle camera (24, 26, 28); generating (208) a first camera lens contamination map corresponding to pixel texture richness and color spectrum levels extracted from the image; Obtaining (212) a sequential input of a plurality of frames from the vehicle camera (24, 26, 28); generating (216) a second camera lens contamination map corresponding to at least one salient region extracted from the plurality of frames; Combining (220) the first camera lens contamination map and the second camera lens contamination map to generate a combined camera lens contamination map; and Initiating (236) an automatic lens cleaning and / or a camera lens blocking signal according to the combined camera lens contamination map. [2] The method of claim 1, wherein initiating (236) an automatic lens cleaning comprises dispensing a cleaning solution to a lens of the vehicle camera (24, 26, 28) and / or moving a windshield wiper across a surface of the lens. [3] The method of claim 1, wherein initiating (236) the camera lens blockage signal comprises warning at least one vehicle motion control system of a blockage of a lens of the vehicle camera (24, 26, 28) to prevent the generation of automatic vehicle braking and / or audible warnings corresponding to the input received from the vehicle camera (24, 26, 28). [4] The method of claim 1, further comprising: Extracting (308) texture sub-regions of the image; Discretizing (312) an image blur degree corresponding to the pixel texture richness of the texture sub-areas; and Extracting (316) color spectrum sub-areas of the image, wherein the first camera lens contamination map is generated by combining the image blur level and the color spectrum sub-ranges. [5] The method of claim 1, further comprising: Extracting and tracking (416) the at least one salient region in the plurality of frames; and Detecting (420) at least one blocking area of the vehicle camera (24, 26, 28) corresponding to the at least one striking area, wherein the second camera lens contamination map is generated corresponding to the at least one blocking area (804). [6] The method of claim 5, further comprising: Obtaining (408) at least one vehicle movement signal; and Generating (412) a timeline of pixel statistics in the plurality of frames corresponding to the at least one vehicle motion signal, wherein the at least one salient region is tracked according to the timeline of the pixel statistics in the plurality of frames, and wherein at least one blocking region (804) is detected according to the timeline of the pixel statistics in the plurality of frames. [7] The method of claim 5, wherein tracking (416) the at least one salient region comprises identifying a location in the plurality of frames at which detected objects repeatedly disappear during vehicle movement. [8] The method of claim 6, wherein the at least one vehicle motion signal comprises a steering wheel rotation signal and / or a wheel speed sensor signal. [9] A system for detecting a blockage of a lens of a vehicle camera (24, 26, 28), comprising: a vehicle camera (24, 26, 28) mounted on a vehicle (10), the vehicle camera (24, 26, 28) having a lens, the vehicle camera (24, 26, 28) being configured to capture images; and a vehicle control module (20) in communication with the vehicle camera (24, 26, 28), the vehicle control module (20) being configured to: receives an image from the vehicle camera (24, 26, 28); Texture sub-areas of the image extracted; discretizes an image blur level according to the pixel texture richness of the texture sub-areas; Color spectrum sub-areas of the image extracted; generates a camera lens contamination map according to the pixel texture richness of the texture sub-regions and the color spectrum sub-regions of the image; and an automatic lens cleaning and / or a camera lens blocking signal is initiated according to the camera lens contamination map.

Images