CN113468363A - Target detection acceleration method for remote sensing image - Google Patents
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Abstract
The present disclosure provides a method for accelerating target detection of a remote sensing image, which includes: acquiring input data of a remote sensing image; segmenting the input data to obtain at least one input subarea; calculating a hash value of the input sub-region; searching a corresponding value of the hash value in the input subarea in a cache according to the hash value; and determining a processing task, and processing the input sub-area according to the processing task to obtain output data. According to the method for accelerating the target detection of the remote sensing image, the input subarea is processed according to the processing task, so that the effect of saving redundant calculation overhead is achieved, and the processing speed of the target detection model of the remote sensing image is improved.
Description
Technical Field
The disclosure relates to the field of processing of remote sensing images of neural networks, in particular to a target detection acceleration method for a remote sensing image.
Background
Remote sensing images are widely applied to different scenes of various industries such as agriculture, archaeology, geology, city planning and the like at present. However, the size of the remote sensing image is far larger than that of a common image, and the number and the spatial imbalance of the target are presented in the image, so that the processing speed of target detection of the remote sensing image becomes a great bottleneck, and the improvement of the target detection speed of the remote sensing image becomes a problem to be solved urgently.
For the classical target detection model, a lot of optimization work is generated, so that the processing speed can achieve the real-time effect. For remote sensing images, the existing method still has certain limitations: (1) the classical model optimization method does not utilize the characteristics of remote sensing images, a plurality of optimization spaces are not effectively utilized, and the purpose of optimization cannot be achieved to the maximum extent; (2) the optimization method aiming at a specific target has excellent effect on the specific target, but has no effect on other targets, and lacks of general applicability; (3) although the filter based on network training can well remove the remote sensing image block which does not contain the target object, a large amount of time is consumed for training, and the brought additional cost is large.
Disclosure of Invention
Technical problem to be solved
The present disclosure provides a method for accelerating target detection of a remote sensing image, so as to at least solve the problems in the prior art.
(II) technical scheme
In order to achieve the above object, the present disclosure provides a method for accelerating target detection of a remote sensing image, including:
acquiring input data of a remote sensing image;
segmenting the input data to obtain at least one input subarea;
calculating a hash value of the input sub-region;
searching a corresponding value of the hash value in the input subarea in a cache according to the hash value;
and determining a processing task, and processing the input sub-area according to the processing task to obtain output data.
In some embodiments of the present disclosure, the calculating the hash value of the input sub-region comprises:
flattening the input sub-region into a one-dimensional vector, calculating a hash value of the one-dimensional vector based on a stochastic projected locality sensitive hash function,
the formula of the locality sensitive hash function h (x) is as follows:
wherein x is represented as the one-dimensional vector and v is represented as a random vector satisfying a standard gaussian distribution.
In some embodiments of the present disclosure, the looking up, in a cache, a corresponding value of the hash value in the input subregion according to the hash value includes:
forming a one-dimensional binary vector by the calculated values of the N locality sensitive hash functions, wherein N is an integer greater than or equal to 1;
and regarding the one-dimensional binary vector as a binary number, and taking an integer value corresponding to the binary number as a key value to search a corresponding value of the hash value in the input sub-area.
In some embodiments of the present disclosure, the determining a processing task, and processing the input sub-region according to the processing task to obtain output data includes:
determining that the processing task is a filtering task, and only storing the key value in a cache;
and determining that the corresponding value exists in the cache, and directly taking the empty result as output data.
In some embodiments of the present disclosure, the determining a processing task, and processing the input sub-region according to the processing task to obtain output data, further includes:
determining that the processing task is a filtering task, and only storing the key value in a cache;
determining that the corresponding value does not exist in the cache, inputting the input sub-region into a corresponding calculation module for calculation, and outputting a calculation result, wherein the calculation module is a network layer for inputting vertex coordinates of a rectangular frame for calibrating a target region to be detected, the calculation module comprises a single-layer calculation module consisting of a single network layer or a plurality of calculation modules consisting of continuous network layers, and the calculation result is the vertex coordinates of the rectangular frame for marking the target to be detected;
and determining that the calculation result is empty, and storing the hash value of the input sub-region into the cache, wherein the cache is in a hash table structure.
In some embodiments of the present disclosure, the determining a processing task, and processing the input sub-region according to the processing task to obtain output data, further includes:
determining that the processing task is a reuse task, and storing a calculation result of the input sub-region after calculation by a calculation module in the cache;
and determining that the corresponding value exists in the cache, and directly taking a calculation result of the input sub-region stored in the cache after calculation by a calculation module as output data, wherein the calculation module comprises a single-layer calculation module consisting of a single network layer or a calculation module consisting of a plurality of continuous network layers.
In some embodiments of the present disclosure, the determining a processing task, and processing the input sub-region according to the processing task to obtain output data, further includes:
determining that the processing task is a reuse task, and storing a calculation result of the input sub-region after calculation by a calculation module in the cache;
determining that the corresponding value does not exist in the cache, inputting the input sub-region into a corresponding calculation module for calculation and outputting a calculation result, wherein the calculation module is a single-layer calculation module consisting of a single network layer or a calculation module consisting of a plurality of continuous network layers;
and determining that the calculation result is not empty, taking the hash value of the input sub-region as a key value, and taking the calculation result as a storage value to be added into the cache.
In some embodiments of the present disclosure, further comprising:
and arranging the calculation results according to the relative position of the input sub-area in the original input, wherein the relative position in the original input refers to the position corresponding to the input sub-area.
In some embodiments of the present disclosure, the segmenting the input data into at least one input sub-region includes:
representing the size of the input sub-region as a sub-value;
representing the size of the original input as an original value;
dividing the input data or filling 0 element in the input data to make the original value be integral multiple of the sub-value;
wherein each input sub-region is a square region with the same size.
(III) advantageous effects
According to the technical scheme, the remote sensing image target detection acceleration method disclosed by the invention at least has one or part of the following beneficial effects:
(1) according to the method for accelerating the target detection of the remote sensing image, the input subarea is processed according to the processing task, so that the effect of saving redundant calculation overhead is achieved, and the processing speed of the target detection model of the remote sensing image is improved.
(2) According to the remote sensing image target detection acceleration method, the Hash value of the input data is used as the searching key value of the cache, a large amount of input data which do not contain the target object can be removed, the calculation result in the network layer is reused by detecting the similarity of the target object, the method has the advantages of universality and plug-and-play, and is suitable for various application scenes of remote sensing image target detection.
Drawings
FIG. 1 is a schematic diagram of a method for accelerating target detection in a remote sensing image according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of the substeps of operation S4 in FIG. 1;
fig. 3 is a schematic diagram of the sub-steps of operation S2 in fig. 1.
Detailed Description
The present disclosure provides a method for accelerating target detection of a remote sensing image, which includes: acquiring input data of a remote sensing image; dividing input data to obtain at least one input subarea; calculating a hash value of the input sub-region; searching a corresponding value of the hash value in the input subarea in the cache according to the hash value; and determining a processing task, and processing the input sub-area according to the processing task to obtain output data. The method can remove a large amount of input data which do not contain the target object, and reuses the calculation result in the network layer by detecting the similarity of the target object; the effect of saving redundant calculation overhead is achieved, and the target detection speed of the remote sensing image is further improved.
For the purpose of promoting a better understanding of the objects, aspects and advantages of the present disclosure, reference is made to the following detailed description taken in conjunction with the accompanying drawings. This disclosure may, however, be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. In the drawings, the size and relative sizes of layers and regions may be exaggerated for clarity, and like reference numerals designate like elements throughout.
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
The present disclosure provides a method for accelerating target detection in remote sensing images, as shown in fig. 1, the method includes operations S1 to S5:
operation S1: acquiring input data of a remote sensing image;
operation S2: dividing input data to obtain at least one input subarea;
operation S3: calculating a hash value of the input sub-region;
operation S4: searching a corresponding value of the hash value in the input subarea in the cache according to the hash value;
operation S5: and determining a processing task, and processing the input sub-area according to the processing task to obtain output data.
The remote sensing image target detection acceleration method processes the input subarea according to the processing task, so that the effect of saving redundant calculation overhead is achieved, and the processing speed of the remote sensing image target detection model is improved.
In operation S3, the calculating the hash value of the input sub-region includes:
flattening the input sub-region into a one-dimensional vector, calculating the hash value of the one-dimensional vector based on the locality sensitive hash function of random projection,
the formula for the locality sensitive hash function h (x) is:
where x is represented as a one-dimensional vector and v is represented as a random vector satisfying a standard gaussian distribution.
In operation S4, as shown in fig. 2, finding the corresponding value of the hash value in the input sub-region in the cache according to the hash value includes operations S41 and S42 as follows:
operation S41: and combining the calculated values of the N locality sensitive hash functions into a one-dimensional binary vector, wherein N is an integer greater than or equal to 1.
Operation S42: and taking the one-dimensional binary vector as a binary number, and taking an integer value corresponding to the binary number as a key value to search a corresponding value of the hash value in the input sub-area.
The hash value of the input data is used as the cached searching key value, a large amount of input data without the target object can be removed, the calculation result in the network layer is reused by detecting the similarity of the target object, and the method has the advantages of universality and plug-and-play, and is suitable for various application scenes of remote sensing image target detection.
In operation S5, determining a processing task, and processing the input sub-region according to the processing task to obtain output data includes:
and determining that the processing task is a filtering task, and only storing the key value in the cache.
And determining that a corresponding value exists in the cache, and directly taking the empty result as output data. And if the output data is a null result, the result indicates that no effective target exists in the cache.
As another embodiment, in operation S5, determining a processing task, and processing the input sub-region according to the processing task to obtain output data, further includes:
and determining that the processing task is a filtering task, and only storing the key value in the cache.
Determining that no corresponding value exists in the cache, inputting the input sub-region into a corresponding calculation module for calculation and outputting a calculation result, wherein the calculation module is a network layer for inputting vertex coordinates of a rectangular frame for calibrating a target region to be detected, the calculation module is a single-layer calculation module consisting of a single network layer or a calculation module consisting of a plurality of continuous network layers, and the calculation result is the vertex coordinates of the rectangular frame for marking the target to be detected;
and determining that the calculation result is null, and storing the hash value of the input sub-region into a cache, wherein the cache is in a hash table structure.
As another embodiment, in operation S5, determining a processing task, and processing the input sub-region according to the processing task to obtain output data, further includes:
and determining the processing task as a reuse task, and storing a calculation result of the input sub-region after calculation by the calculation module in the cache.
And determining that the corresponding value exists in the cache, and directly taking a calculation result of the input sub-region after calculation by the calculation module as output data, wherein the calculation module comprises a single-layer calculation module consisting of a single network layer or a calculation module consisting of a plurality of continuous network layers.
As another embodiment, in operation S5, determining a processing task, and processing the input sub-region according to the processing task to obtain output data, further includes:
and determining the processing task as a reuse task, and storing a calculation result of the input sub-region after calculation by the calculation module in the cache.
And determining that no corresponding value exists in the cache, inputting the input sub-region into a corresponding calculation module for calculation and outputting a calculation result, wherein the calculation module is a single-layer calculation module consisting of a single network layer or a calculation module consisting of a plurality of continuous network layers.
And determining that the calculation result is not null, using the hash value of the input sub-region as a key value, and using the calculation result as a storage value to be added into the cache.
As another embodiment of the present disclosure, in the remote sensing image target detection acceleration method of the present disclosure, operation S5: determining a processing task, and after processing the input sub-region according to the processing task to obtain output data, the method further comprises the following steps:
and arranging the calculation results according to the relative position of the input subarea in the original input, wherein the relative position in the original input refers to the position corresponding to the input subarea, and the calculation results comprise the calculation results which are hit in the cache and taken out of the cache, and also comprise the calculation results which are not hit in the cache and calculated by the calculation module.
In operation S2, as shown in fig. 3, the input data is divided into at least one input sub-region, which includes the following operations S21-S23:
s21: the size of the input sub-region is represented as a sub-value.
S22: the size of the original input is expressed as the original value.
S23: the input data is divided or 0 elements are filled in the input data so that the original value is an integral multiple of the sub-value.
Wherein each input sub-region is a square region with the same size.
The input remote sensing images with inconsistent lengths can be filled with 0 or cut to enable the remote sensing images to meet corresponding conditions.
The calculation result in the network layer is reused by detecting the similarity of the target object, and the method has the advantages of universality and plug-and-play because no additional training process is needed and the method does not aim at a specific target to be detected, and is suitable for various application scenes for detecting the remote sensing image target.
It should also be noted that directional terms, such as "upper", "lower", "front", "rear", "left", "right", and the like, used in the embodiments are only directions referring to the drawings, and are not intended to limit the scope of the present disclosure. Throughout the drawings, like elements are represented by like or similar reference numerals. In the event of possible confusion for understanding of the present disclosure, conventional structures or configurations will be omitted, and the shapes and sizes of the components in the drawings do not reflect actual sizes and proportions, but merely illustrate the contents of the embodiments of the present disclosure.
Unless otherwise indicated, the numerical parameters set forth in the specification and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by the present disclosure. In particular, all numbers expressing quantities of ingredients, reaction conditions, and so forth used in the specification and claims are to be understood as being modified in all instances by the term "about". Generally, the expression is meant to encompass variations of ± 10% in some embodiments, 5% in some embodiments, 1% in some embodiments, 0.5% in some embodiments by the specified amount.
The use of ordinal numbers such as "first," "second," "third," etc., in the specification and claims to modify a corresponding element does not by itself connote any ordinal number of the element or any ordering of one element from another or the order of manufacture, and the use of the ordinal numbers is only used to distinguish one element having a certain name from another element having a same name.
In addition, unless steps are specifically described or must occur in sequence, the order of the steps is not limited to that listed above and may be changed or rearranged as desired by the desired design. The embodiments described above may be mixed and matched with each other or with other embodiments based on design and reliability considerations, i.e., technical features in different embodiments may be freely combined to form further embodiments.
The above-mentioned embodiments are intended to illustrate the objects, aspects and advantages of the present disclosure in further detail, and it should be understood that the above-mentioned embodiments are only illustrative of the present disclosure and are not intended to limit the present disclosure, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present disclosure should be included in the scope of the present disclosure.
Claims (9)
1. A remote sensing image target detection acceleration method comprises the following steps:
acquiring input data of a remote sensing image;
segmenting the input data to obtain at least one input subarea;
calculating a hash value of the input sub-region;
searching a corresponding value of the hash value in the input subarea in a cache according to the hash value;
and determining a processing task, and processing the input sub-area according to the processing task to obtain output data.
2. The remote sensing image target detection acceleration method of claim 1, wherein the calculating the hash value of the input subregion comprises:
flattening the input sub-region into a one-dimensional vector, calculating a hash value of the one-dimensional vector based on a stochastic projected locality sensitive hash function,
the formula of the locality sensitive hash function h (x) is as follows:
wherein x is represented as the one-dimensional vector and v is represented as a random vector satisfying a standard gaussian distribution.
3. The remote sensing image target detection acceleration method according to claim 2, wherein the searching for the corresponding value of the hash value in the input subregion in the cache according to the hash value comprises:
forming a one-dimensional binary vector by the calculated values of the N locality sensitive hash functions, wherein N is an integer greater than or equal to 1;
and regarding the one-dimensional binary vector as a binary number, and taking an integer value corresponding to the binary number as a key value to search a corresponding value of the hash value in the input sub-area.
4. The remote sensing image target detection acceleration method according to claim 3, wherein the determining a processing task, processing the input sub-region according to the processing task to obtain output data, comprises:
determining that the processing task is a filtering task, and only storing the key value in a cache;
and determining that the corresponding value exists in the cache, and directly taking the empty result as output data.
5. The method for detecting and accelerating the remote sensing image target according to claim 3, wherein the determining a processing task and processing the input sub-region according to the processing task to obtain output data further comprises:
determining that the processing task is a filtering task, and only storing the key value in a cache;
determining that the corresponding value does not exist in the cache, inputting the input sub-region into a corresponding calculation module for calculation, and outputting a calculation result, wherein the calculation module is a network layer for inputting vertex coordinates of a rectangular frame for calibrating a target region to be detected, the calculation module comprises a single-layer calculation module consisting of a single network layer or a plurality of calculation modules consisting of continuous network layers, and the calculation result is the vertex coordinates of the rectangular frame for marking the target to be detected;
and determining that the calculation result is empty, and storing the hash value of the input sub-region into the cache, wherein the cache is in a hash table structure.
6. The method for detecting and accelerating the remote sensing image target according to claim 1, wherein the determining a processing task and processing the input sub-region according to the processing task to obtain output data further comprises:
determining that the processing task is a reuse task, and storing a calculation result of the input sub-region after calculation by a calculation module in the cache;
and determining that the corresponding value exists in the cache, and directly taking a calculation result of the input sub-region stored in the cache after calculation by a calculation module as output data, wherein the calculation module comprises a single-layer calculation module consisting of a single network layer or a calculation module consisting of a plurality of continuous network layers.
7. The method for detecting and accelerating the remote sensing image target according to claim 1, wherein the determining a processing task and processing the input sub-region according to the processing task to obtain output data further comprises:
determining that the processing task is a reuse task, and storing a calculation result of the input sub-region after calculation by a calculation module in the cache;
determining that the corresponding value does not exist in the cache, inputting the input sub-region into a corresponding calculation module for calculation and outputting a calculation result, wherein the calculation module is a single-layer calculation module consisting of a single network layer or a calculation module consisting of a plurality of continuous network layers;
and determining that the calculation result is not empty, taking the hash value of the input sub-region as a key value, and taking the calculation result as a storage value to be added into the cache.
8. The remote sensing image target detection acceleration method according to claim 7, further comprising:
and arranging the calculation results according to the relative position of the input sub-area in the original input, wherein the relative position in the original input refers to the position corresponding to the input sub-area.
9. The remote sensing image target detection acceleration method of claim 1, wherein the segmenting the input data into at least one input subregion comprises:
representing the size of the input sub-region as a sub-value;
representing the size of the original input as an original value;
dividing the input data or filling 0 element in the input data to make the original value be integral multiple of the sub-value;
wherein each input sub-region is a square region with the same size.
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