## 4 Conclusions  
Research has demonstrated that the proposed innovative adaptive approach to the selection of images acquired by ground-penetrating radar enabled a significant improvement of the efficiency of the detection of hyperbolas representing underground utility networks, by 15-40% in comparison to the detection performed in the lowest-quality images and by 10-20% in comparison to the average values of efficiency. High values of entropy, presented on the map of image quality assessment, were noted in the locations where underground utility networks are present. The hyperbolas in the binary image clearly overlap with the areas with increased values of entropy that represent higher quality (characterized by a noticeable presence of useful signals) fragments of the radargrams. High values of entropy are also a measure of the quality of the GPR signal. Areas characterized by low levels of entropy may indicate either a homogeneous ground medium or noise and the loss of GPR signal caused by higher attenuation of the ground medium. Using entropy to superimpose quality maps on binary images not only improves the clarity but also enables the employment of machine learning algorithms for the identification and classification of the detected objects. Images characterized by high entropy may be automatically filtered as potential locations of underground infrastructure, which will allow to shorten the time required to perform manual analyses of the acquired GPR data. Areas characterized by lower entropy values may indicate a lower quality of data resulting from a smaller number of objects detected in the image. The application of quality indicators (entropy, PIQE, and Laplacian variance) allows for a reliable assessment of the degree of blurring, noise, and the number of details that are present in the analyzed images. The proposed approach enables a quick evaluation of the quality of radargrams (that are characterized by a clear presence of usable signals), which supports the process of detecting underground objects. In most of the analyzed radargrams, all the existing utility infrastructure was detected. The number of undetected  
Figure 14: Image quality assessment maps generated for the lowest and highest values of entropy for route 3 with the verification of the detected underground objects. utility networks was higher in images characterized by a high degree of blurring (low values of Laplacian variance). The proposed adaptive approach enables to shorten the time of processing GPR data, while it also indicates the radargrams with noticeable useful signals. The selection of these images improves the quality of the detection of hyperbolas in images. The approach based on non-reference image quality assessment methods enables the adaptive selection of images of the highest quality, even before the process of the automated detection of hyperbolas. In spite of their multiple advantages, the applied quality assessment methods also have certain limitations. One of them is the fact that a single value of the quality indicator (entropy, PIQE, or Laplacian variance) is determined for the whole image. The global image quality assessment does not take into account local distortions and artifacts that might have a significant influence on the process of detecting hyperbolas. Considering the above limitations, the authors are planning future research to develop a new approach to image quality assessment, based on sector-specific indicators. This will allow for the determination of the quality of radargrams within parts of the image, i.e., blocks. The sector-specific radargram quality assessment may significantly improve the efficiency of quality assessment, which will likely lead to an improvement in the quality of the detection of underground infrastructure. This approach will probably enable us to overcome the limitations of global quality assessment methods, constituting a basis for further research on the methods of the detection of hyperbolas in GPR images of diverse quality parameters. Conceptualization: K.P.