# CTIMS: Automated Defect Detection Framework Using Computed Tomography
## 7 Conclusions and Future Work  
This work explored a new computer-aided diagnosis toolbox, called CTIMS-Toolbox, for X-ray CT data inspection. The CTIMS-Toolbox is based on a hybrid framework integrating computer vision and deep-learning-based algorithms. It contains three main modules: the data management, pre-processing, and defect inspection modules. The toolbox allows the input CT scan data to obtain an automated inspection report based on three detection steps: prediction, localization, and characterization. It can incrementally learn from the user experience to generate annotated data that are used to train the deep learning models further and improve their performance. The latter helps build a valuable knowledge base that can help accelerate the inspection operation and provide preventive insights about the tool's safety for future usage. However, the different CTIMS-Toolbox modules have some limitations related to the database management, especially for a big data size, the quality distortion after the pre-processing step, the long execution time, and the parameter tuning for the inspection module. These limitations can be further improved as follows:  
**Data management**: While the design of the database can certainly be restructured, there is significant potential to completely redefine the database framework by using big-data-type structures, such as graph-based databases [47; 48] or columnar databases [49]. \begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**System**} & \multicolumn{2}{c}{**Defect**} & \multirow{2}{*}{**Commercial**} & \multirow{2}{*}{**Remarks**} \\  & **Localization** & & & & \\ \cline{2-4}  & **DSP** & **DNN** & & & \\ \hline \multirow{2}{*}{_DIRA-GREEN [15]_} & \multirow{2}{*}{✓} & \multirow{2}{*}{[[https://www.youtube.com/watch/be/6m3](https://www.youtube.com/watch/be/6m3) RGNN7Q]([https://www.youtube.com/watch/be/6m3](https://www.youtube.com/watch/be/6m3) RGNN7Q)} & + automated system & \\  & & & (accessed on 10 December 2021) & - only for small-sized tools \\ \hline \multirow{2}{*}{_Autmental Defect_[14]_} & \multirow{2}{*}{✓} & \multirow{2}{*}{✓} & \multirow{2}{*}{✓} & \multirow{2}{*}{✓} \\  & & & & & \\ \multirow{2}{*}{_Recognition (ADR)_} & & & & & \\  & & & & & \\ \hline \multirow{2}{*}{_RobFlow [46]_} & \multirow{2}{*}{✓} & \multirow{2}{*}{✓} & \multirow{2}{*}{✓} & \multirow{2}{*}{✓} \\  & & & & & \\ \hline \multirow{2}{*}{_ADR X-ray inspection [30]_} & \multirow{2}{*}{✓} & \multirow{2}{*}{✓} & \multirow{2}{*}{✓} & \multirow{2}{*}{✓} \\  & & & & & \\ \end{tabular}
\end{table} 
Table 2: Comparison of existing CT inspection algorithms and systems. **Preprocessing module**: The integrated background subtraction and registration are implemented and tested on scans of one tool's data. In the future, when different tools and scanning machines will come into use, we will work on the automation of the pre-processing module by the adaptive parameter selection based on the input CT data. In addition, we will optimize the implementation to reduce the execution time and storage capacity. **Inspection module**: We will focus on designing an adaptive selection of the inspection algorithm's parameters to fit all kinds of variability in X-ray CT data. Thus, we will use a deep regression model to estimate the optimal input parameter based on the input data features: noise level, resolution, contrast, etc. Finally, the proposed CTIMS-Toolbox can inspect 2D and 3D CT data and generate annotations based on the inspection results after validation by an expert. With a proper configuration of the scanner parameters, beam energy, and detector resolution, the proposed toolbox can be used for most materials such as metal, plastic, wood, etc. [PERSON] is the Principle Investigator, designed the idea of whole system automation process, and participated in the design of the functions, system integration, CT technology, and algorithm development and validation within the CTIMS-Toolbox. [PERSON] designed and implemented the database management of the toolbox.