** compiler warnings due to subsequent content in this file and other files ** This should be done first so that it can successfully prevent spurious
** Include the header file used to customize the compiler options for MSVC. * ** Make sure that rand_s() is available on Windows systems with MSVC 2005 ** or higher. ** only used by test code and Tcl integration code. ** Make sure the Tcl calling convention macro is defined.
#endif #if SQLITE_ENABLE_BATCH_ATOMIC_WRITE "ENABLE_BATCH_ATOMIC_WRITE",#endif "ENABLE_8_3_NAMES=" CTIMEOPT_VAL(SQLITE_ENABLE_8_3_NAMES), By combining all the individual C code files into this ** This file is all you need to compile SQLite. ** of 5% or more are commonly seen when SQLite is compiled as a single ** possible if the files were compiled separately. This allows many compilers to do optimizations that would not be ** single large file, the entire code can be compiled as a single translation ** This file is an amalgamation of many separate C source files from SQLite Modified sqlite3/src/main/jni/sqlite/sqlite3.c in the individual module adle filesĬlasspath ':gradle:2.3. NOTE: Do not place your application dependencies here they belong Using those aforementioned strategies with our proposed encoder-decoder architecture we are able to achieve new state of the art results in all datasets.// Top-level build file where you can add configuration options common to all sub-projects/modules.Ĭlasspath ':gradle:2.3. The former approach generally yields increased performance results, whereas the latter allows for more reproducible and better representability of a methods results. Additionally, we introduce two techniques into the field of surface crack segmentation, previously not used there: Generating results using test-time-augmentation and performing a statistical result analysis over multiple training runs.
The performance evaluation of our method is carried out on four publicly available crack segmentation datasets. We also examine the use of different encoder strategies and introduce a data augmentation policy to increase the amount of available training data. Specifically we propose a decoder-part for an encoder-decoder based deep learning architecture for semantic segmentation and study its components to achieve increased performance. In this work we propose optimized deep encoder-decoder methods consisting of a combination of techniques which yield an increase in crack segmentation performance. Surface crack segmentation poses a challenging computer vision task as background, shape, color and size of cracks vary.
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