# Detection and Evaluation of Construction Cracks through Image Analysis Using Computer Vision
## 4 Conclusions  
This study demonstrates the feasibility of utilizing a specialized neural network model, YOLOv4, to effectively detect and assess various types of cracks and fractures in concrete  
Figure 13: (**a**) IMG1076 with the bounding box. (**b**) Extracted section corresponding to the bounding box. (**c**) Image converted to negative grayscale.  
beams and columns. The achieved precision, measured by mean average precision (mAP), ranged from 96.62% to 99.75%. The model was trained on a modest set of high-resolution images (792 images, 3840 \(\times\) 2160 pixels, 96 dpi).  
To further enhance the neural network's performance, the following recommendations are proposed:  
* Consider adopting more recent YOLO model iterations, such as YOLOv6 or YOLOv7.
* Fine-tune the model's operational parameters to prioritize performance gains over shorter training times.
* Apply pruning to reduce the computational cost of the neural network, as well as other preprocessing and data augmentation techniques.
* Expand the training dataset significantly, encompassing over 2000 images.
* Introduce greater diversity to the images, incorporating different concrete compositions, varied angles, and lighting conditions.  
Concurrently, an alternative mechanism demanding minimal computational resources has been suggested for evaluating fracture criticality. This approach involves a grayscale conversion and pixel chromatic value comparison to gauge the significance of a fracture based on pixels near the target in a converted grayscale image. A comprehensive analysis is recommended to validate the proposed method's precision, contrasting automatic pixel-based assessments with manual or visual evaluations.  
However, it is important to note some limitations of the proposed method that could be part of relevant future research:  
* The method is limited to static evaluations and does not account for dynamic fracture changes over time.
* Factors like the size of the analyzed structural element, lighting conditions, porosity of the building material, and camera quality and positioning influence the outcomes. Additional experimentation with these variables is essential to establish standardized fracture criticality scales.
* The implemented methodology is limited to applying the pre-existing YOLOv4 algorithm in a customized database for detecting and evaluating cracks in construction, so preprocessing techniques such as pruning have not been applied.  
In conclusion, the proposed mechanism is not designed to offer an exact quantitative assessment of crack size. Instead, its primary purpose is to identify regions warranting closer in situ examination. This application holds particular significance for hard-to-reach structures like bridges or dams, where automated preliminary identification via drones proves advantageous.  
Conceptualization, A.A.D.S.; Methodology, A.A.D.S., A.L.T., D.C.S., M.A.V.O. and G.T.U.I; Software, D.C.S. and G.T.U.I; Validation, M.A.V.O., A.L.T., A.A.D.S. and D.C.S.; Formal Analysis, D.C.S., A.L.T. and M.A.V.O.; Investigation, G.T.U.I. and M.A.V.O.; Resources, A.A.D.S., A.L.T. and D.C.S.; Data Curation, M.A.V.O.; Writing--Original Draft Preparation, M.A.V.O. and G.T.U.I; Writing--Review and Editing, A.A.D.S., A.L.T. and D.C.S.; Visualization, M.A.V.O. and G.T.U.I; Supervision, A.A.D.S. and A.L.T.; Project Administration, A.A.D.S.; Funding Acquisition, A.A.D.S. All authors have read and agreed to the published version of the manuscript.  
This article was developed as part of the \"Detection of construction failures by analyzing frames using AI\" research project, funding number AC.06.016.2022, of the Scientific Research Institute (IDIC) of Universidad de Lima.  
The data supporting the findings of this study are available within the article.  
The authors thank the Universidad de Lima for supporting this research.  
The authors declare no conflict of interest.