Software interface element detection method and system based on multi-granularity learning, computer equipment and storage medium
A software interface and detection method technology, applied in software testing/debugging, computing, error detection/correction, etc., can solve problems such as low efficiency, human factors, error-prone, etc., to reduce interference, ensure detection effectiveness, The effect of improving efficiency and accuracy
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Publication Date
- 2020-05-05
Smart Images

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Abstract
Description
technical field
[0001] The invention relates to a multi-granularity-based software interface element detection method, system, computer equipment and storage medium, and belongs to the field of automation software auxiliary technology. Background technique
[0002] Automatic detection of software interface elements is the key technology of software intelligence. Software intelligence and some software auxiliary fields need to be able to automatically detect software interface elements. However, with the update iteration of software interface and the continuous enrichment of software interface element types, the complexity of automatic control detection is also increasing.
[0003] In the process of software development and testing, it is also necessary to detect the software interface, that is, to detect whether the software interface is consistent with the software interface in the design drawing. At present, it is mainly necessary to rely on testers to check whether the s...
Examples
Embodiment example
[0079] Step (1), input the image of the software interface; input an image, the image is an RGB three-channel image, and the size is W*H.
[0080] Step (2), use the convolutional neural network to obtain the features of the image, and obtain an intermediate result of the same size; the intermediate result is an image of D*W*H.
[0081] Step (3), perform multi-granularity meshing operation on the features obtained in step (2); suppose the granularity size is (a 1 *b 1 ),(a 2 *b 2 ), (a 3 *b 3 )……(a n *b n ), then n groups of image grids of different sizes are obtained respectively. In this embodiment, assuming that the granularity is (4x4), (8x8), (16x16), three sets of grids can be obtained: the first set of 16 grids (4x4 equal parts of the original image), the second set of 64 grids, and the third group of 256 grids.
[0082] Step (4), use a multi-grain cascade background discriminator to calibrate the non-control background grid area of the software interface; ass...