CN113468363B - Remote sensing image target detection acceleration method - Google Patents
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Abstract
The disclosure provides a remote sensing image target detection acceleration method, comprising the following steps: acquiring input data of a remote sensing image; dividing the input data to obtain at least one input subarea; calculating a hash value of the input subarea; searching a corresponding value of the hash value in the input subarea in a cache according to the hash value; determining a processing task, and processing the input subarea according to the processing task to obtain output data. According to the remote sensing image target detection acceleration method, the input subareas are processed according to the processing task, so that the effect of saving redundant calculation overhead is achieved, and the processing speed of a remote sensing image target detection model is improved.
Description
Technical Field
The disclosure relates to the field of remote sensing image processing of neural networks, in particular to a remote sensing image target detection acceleration method.
Background
Remote sensing images are widely applied to different scenes in a plurality of industries such as agriculture, archaeology, geology, urban planning and the like. However, the size of the remote sensing image is far beyond that of an ordinary image, and the targets show the unbalance in quantity and space in the image, so that the processing speed of target detection of the remote sensing image becomes a big bottleneck, and the problem to be solved is that the target detection speed of the remote sensing image is improved.
For classical target detection models, a lot of optimization work has been done so that the processing speed can reach real-time effects. For remote sensing images, the existing methods still have 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 the specific target has excellent effect on the specific target, but cannot be used for other targets, and lacks versatility; (3) Although the filter based on network training can well remove the remote sensing image block which does not contain the target object, a great deal of time is consumed for training, and the additional expense is large.
Disclosure of Invention
First, the technical problem to be solved
The present disclosure proposes a remote sensing image target detection acceleration method to at least solve the above-mentioned problems in the prior art.
(II) technical scheme
In order to achieve the above object, the present disclosure provides a remote sensing image target detection acceleration method, including:
Acquiring input data of a remote sensing image;
dividing the input data to obtain at least one input subarea;
calculating a hash value of the input subarea;
searching a corresponding value of the hash value in the input subarea in a cache according to the hash value;
Determining a processing task, and processing the input subarea according to the processing task to obtain output data.
In some embodiments of the present disclosure, the computing the hash value of the input subregion includes:
Flattening the input subarea into a one-dimensional vector, calculating a hash value of the one-dimensional vector based on a stochastic projected locality sensitive hash function,
The calculation formula of the locality sensitive hashing function h (x) is as follows:
where 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 disclosure, the searching the buffer for the corresponding value of the hash value in the input subregion according to the hash value includes:
Forming one-dimensional binary vectors by the calculated values of N local 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 searching for the corresponding value of the hash value in the input subarea by taking the integer value corresponding to the binary number as a key value.
In some embodiments of the present disclosure, the determining a processing task, processing the input sub-region according to the processing task to obtain output data includes:
determining the processing task as a filtering task, and storing only 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, processing the input sub-region according to the processing task to obtain output data, further includes:
determining the processing task as a filtering task, and storing only the key value in a cache;
Determining that the corresponding value does not exist in the cache, inputting the input subarea into a corresponding calculation module to calculate 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 area to be detected, the calculation module comprises a single-layer calculation module formed by a single network layer or a calculation module formed by 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 subarea into the cache, wherein the cache is in a hash table structure.
In some embodiments of the present disclosure, the determining a processing task, processing the input sub-region according to the processing task to obtain output data, further includes:
Determining the processing task as a reuse task, and storing a calculation result of the input subarea 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 subarea stored in the cache after calculation by a calculation module as output data, wherein the calculation module comprises a single-layer calculation module formed by a single network layer or a calculation module formed by a plurality of continuous network layers.
In some embodiments of the present disclosure, the determining a processing task, processing the input sub-region according to the processing task to obtain output data, further includes:
Determining the processing task as a reuse task, and storing a calculation result of the input subarea after calculation by a calculation module in the cache;
Determining that the corresponding value does not exist in the cache, inputting the input subarea into a corresponding calculation module for calculation and outputting a calculation result, wherein the calculation module is a single-layer calculation module formed by a single network layer or a calculation module formed by a plurality of continuous network layers;
and determining that the calculation result is not null, taking the hash value of the input subarea as a key value, taking the calculation result as a storage value, and adding the calculation result into the cache.
In some embodiments of the present disclosure, further comprising:
and arranging the calculation results according to the relative positions of the input subareas in the original input, wherein the relative positions in the original input refer to the positions corresponding to the input subareas.
In some embodiments of the present disclosure, the dividing the input data to obtain 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 an integer multiple of the sub value;
Wherein each input subarea is a square area with the same size.
(III) beneficial effects
According to the technical scheme, the remote sensing image target detection acceleration method has at least one or a part of the following beneficial effects:
(1) According to the remote sensing image target detection acceleration method, the input subareas are processed according to the processing task, so that the effect of saving redundant calculation overhead is achieved, and the processing speed of a remote sensing image target detection model is improved.
(2) The remote sensing image target detection acceleration method can remove a large amount of input data which does not contain target objects by taking the hash value of the input data as a cached search key value, reuses the calculation result in a network layer by detecting the similarity of the target objects, has the advantages of universality and plug and play, and is suitable for application scenes of target detection of various remote sensing images.
Drawings
FIG. 1 is a schematic diagram of a remote sensing image target detection acceleration method in an embodiment of the disclosure;
FIG. 2 is a schematic diagram of sub-steps of operation S4 in FIG. 1;
fig. 3 is a schematic diagram of sub-steps of operation S2 in fig. 1.
Detailed Description
The disclosure provides a remote sensing image target detection acceleration method, comprising the following steps: 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 subarea; searching a corresponding value of the hash value in the input subarea in the cache according to the hash value; determining a processing task, and processing the input subarea according to the processing task to obtain output data. The method can remove a large amount of input data which does not contain the target object, and reuse the calculation result in the network layer by detecting the similarity of the target object; the redundant calculation overhead is saved, and the target detection speed of the remote sensing image is further improved.
For the purposes of promoting an understanding of the principles and advantages of the disclosure, reference will now be made to the embodiments illustrated in the drawings and specific language will be used to describe the same. 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 of layers and regions, as well as the relative sizes, may be exaggerated for the same 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 only exemplary 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 present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to 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/or 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 should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a convention should be interpreted in accordance with the meaning of one of skill in the art having generally understood the convention (e.g., "a system having at least one of A, B and C" would include, but not be limited to, systems having 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 formulation similar to at least one of "A, B or C, etc." is used, in general such a formulation should be interpreted in accordance with the ordinary understanding of one skilled in the art (e.g. "a system with at least one of A, B or C" would include but not be limited to systems with 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 remote sensing image target detection acceleration method, 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 subarea;
Operation S4: searching a corresponding value of the hash value in the input subarea in the cache according to the hash value;
Operation S5: determining a processing task, and processing the input subarea according to the processing task to obtain output data.
According to the remote sensing image target detection acceleration method, the input subareas are processed according to the processing task, so that the effect of saving redundant calculation overhead is achieved, and the processing speed of a remote sensing image target detection model is improved.
In operation S3, calculating the hash value of the input sub-region includes:
flattening the input subregion into a one-dimensional vector, calculating a hash value of the one-dimensional vector based on a stochastic projected locality sensitive hash function,
The calculation formula of the locality sensitive hashing 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, searching the buffer for the corresponding value of the hash value in the input sub-region according to the hash value includes the following operations S41 and S42:
Operation S41: and forming the calculated values of the N local sensitive hash functions into a one-dimensional binary vector, wherein N is an integer greater than or equal to 1.
Operation S42: and regarding the binary vector in one dimension as a binary number, and searching for the corresponding value of the hash value in the input subarea by taking the integer value corresponding to the binary number as a key value.
The hash value of the input data is used as the cached search key value, so that a large amount of input data which does 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, and the method has the advantages of universality and plug and play, and is suitable for application scenes of target detection of various remote sensing images.
In operation S5, determining a processing task, processing the input sub-region according to the processing task to obtain output data, including:
And determining the processing task as a filtering task, and storing only 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 empty, indicating that no valid target exists in the cache.
As another embodiment, in operation S5, a processing task is determined, and the input sub-area is processed according to the processing task to obtain output data, and further includes:
And determining the processing task as a filtering task, and storing only 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 to calculate and outputting a calculation result, wherein the calculation module is a network layer for inputting the vertex coordinates of a rectangular frame for calibrating the target region to be detected, the calculation module is a single-layer calculation module formed by a single network layer or a calculation module formed by 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 subarea into a cache, wherein the cache is in a hash table structure.
As another embodiment, in operation S5, a processing task is determined, and the input sub-area is processed according to the processing task to obtain output data, and further includes:
And determining the processing task as a reuse task, and storing the calculation result of the input subarea after calculation by the calculation module in the cache.
And determining that a corresponding value exists in the cache, and directly taking a calculation result obtained after the calculation of the input subarea by a calculation module as output data, wherein the calculation module comprises a single-layer calculation module formed by a single network layer or a calculation module formed by a plurality of continuous network layers.
As another embodiment, in operation S5, a processing task is determined, and the input sub-area is processed according to the processing task to obtain output data, and further includes:
And determining the processing task as a reuse task, and storing the calculation result of the input subarea after calculation by the calculation module in the cache.
Determining that no corresponding value exists in the cache, inputting the input subarea into a corresponding calculation module for calculation and outputting a calculation result, wherein the calculation module is a single-layer calculation module formed by a single network layer or a calculation module formed by a plurality of continuous network layers.
And determining that the calculation result is not null, taking the hash value of the input subarea as a key value, taking the calculation result as a storage value, and adding the storage value into a cache.
As another embodiment of the present disclosure, operation S5 in the remote sensing image target detection acceleration method of the present disclosure: determining a processing task, processing the input subarea according to the processing task to obtain output data, and then further comprising:
And arranging the calculation results according to the relative positions of the input subareas in the original input, wherein the relative positions in the original input refer to the positions corresponding to the input subareas, and the calculation results comprise the calculation results which are hit in a cache and taken out of the cache, and the calculation results which are not hit in the cache and are calculated by a calculation module.
In operation S2, as shown in fig. 3, the input data is divided to obtain at least one input sub-region, including operations S21 to S23 as follows:
S21: the size of the input sub-region is denoted as a sub-value.
S22: the size of the original input is represented as an original value.
S23: the input data is divided or 0 element is filled in the input data, so that the original value is an integer multiple of the sub value.
Wherein each input subregion is a square region of equal size.
For input remote sensing images with inconsistent lengths, 0 can be used for filling the input remote sensing images, or cutting is carried out, so that the remote sensing images meet corresponding conditions.
The method reuses the calculation result in the network layer through detecting the similarity of the target object, and does not aim at a specific target to be detected because no additional training process is needed, so that the method has the advantages of universality and plug and play, and is suitable for application scenes of target detection of various remote sensing images.
It should be further noted that, the directional terms mentioned in the embodiments, such as "upper", "lower", "front", "rear", "left", "right", etc., are only referring to the directions of the drawings, and are not intended to limit the scope of the present disclosure. Like elements are denoted by like or similar reference numerals throughout the drawings. In the event that an understanding of the present disclosure may be made, conventional structures or constructions will be omitted, and the shapes and dimensions of the various parts in the drawings do not reflect actual sizes and proportions, but merely illustrate the contents of the embodiments of the present disclosure.
Unless otherwise known, numerical parameters in this specification and the appended claims are approximations that may 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". In general, the meaning of expression is meant to include a variation of + -10% in some embodiments, a variation of + -5% in some embodiments, a variation of + -1% in some embodiments, and a variation of + -0.5% in some embodiments by a particular amount.
The use of ordinal numbers such as "first," "second," "third," etc., in the description and the claims to modify a corresponding element does not by itself connote any ordinal number of elements or the order of manufacturing or use of the ordinal numbers in a particular claim, merely for enabling an element having a particular name to be clearly distinguished from another element having the same name.
Furthermore, unless specifically described or steps must occur in sequence, the order of the above steps is not limited to the list above and may be changed or rearranged according to the desired design. In addition, the above embodiments may be mixed with each other or other embodiments based on design and reliability, i.e. the technical features of the different embodiments may be freely combined to form more embodiments.
While the foregoing is directed to embodiments of the present disclosure, other and further details of the invention may be had by the present application, it is to be understood that the foregoing description is merely exemplary of the present disclosure and that no limitations are intended to the scope of the disclosure, except insofar as modifications, equivalents, improvements or modifications may be made without departing from the spirit and principles of the present disclosure.
Claims (5)
1. A remote sensing image target detection acceleration method comprises the following steps:
Acquiring input data of a remote sensing image;
dividing the input data to obtain at least one input subarea;
calculating a hash value of the input subarea;
searching a corresponding value of the hash value in the input subarea in a cache according to the hash value, wherein the hash value is used as a searching key value of the cache;
determining a processing task, and processing the input subarea according to the processing task to obtain output data;
The determining a processing task, processing the input sub-region according to the processing task to obtain output data, includes:
determining the processing task as a filtering task, and storing only the key value in a cache;
determining that the corresponding value exists in the cache, and directly taking an empty result as output data;
Determining that the corresponding value does not exist in the cache, inputting the input subarea into a corresponding calculation module to calculate 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 area to be detected, the calculation module comprises a single-layer calculation module formed by a single network layer or a calculation module formed by 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;
Determining that the calculation result is null, and storing the hash value of the input subarea into the cache, wherein the cache is in a hash table structure;
Determining the processing task as a reuse task, and storing a calculation result of the input subarea after calculation by a calculation module in the cache;
Determining that the corresponding value exists in the cache, and directly taking a calculation result of the input subarea stored in the cache after calculation by a calculation module as output data, wherein the calculation module comprises a single-layer calculation module formed by a single network layer or a calculation module formed by a plurality of continuous network layers;
Determining that the corresponding value does not exist in the cache, inputting the input subarea into a corresponding calculation module for calculation and outputting a calculation result, wherein the calculation module is a single-layer calculation module formed by a single network layer or a calculation module formed by a plurality of continuous network layers;
and determining that the calculation result is not null, taking the hash value of the input subarea as a key value, taking the calculation result as a storage value, and adding the calculation result into the cache.
2. The method for accelerating target detection of a remote sensing image according to claim 1, wherein the calculating the hash value of the input sub-region comprises:
Flattening the input subarea into a one-dimensional vector, calculating a hash value of the one-dimensional vector based on a stochastic projected locality sensitive hash function,
The calculation formula of the locality sensitive hashing function h (x) is as follows:
where x is represented as the one-dimensional vector and v is represented as a random vector satisfying a standard gaussian distribution.
3. The method of claim 2, wherein the searching the buffer for the corresponding value of the hash value in the input subarea according to the hash value comprises:
Forming one-dimensional binary vectors by the calculated values of N local 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 searching for the corresponding value of the hash value in the input subarea by taking the integer value corresponding to the binary number as a key value.
4. The remote sensing image target detection acceleration method of claim 1, further comprising:
and arranging the calculation results according to the relative positions of the input subareas in the original input, wherein the relative positions in the original input refer to the positions corresponding to the input subareas.
5. The method of claim 1, wherein the dividing the input data to obtain at least one input sub-region 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 an integer multiple of the sub value;
Wherein each input subarea is a square area with the same size.
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| CN109284741A (en) * | 2018-10-30 | 2019-01-29 | 武汉大学 | A kind of extensive Remote Sensing Image Retrieval method and system based on depth Hash network |
| CN109919108A (en) * | 2019-03-11 | 2019-06-21 | 西安电子科技大学 | Remote sensing images fast target detection method based on depth Hash auxiliary network |
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