This paper presents a comparative study of two algorithms for detecting and analyzing 29 the characteristic shapes of reflection obtained as a result of Ground-Penetrating Radar (GPR) 30 scanning technology. The first algorithm is a sub-array processing method that uses direction-of31 arrival algorithms and the matched filter technique; this approach is implemented in SPOT-GPR 32 (release 1.0), a new freeware tool for the detection and localization of targets in radargrams. The 33 second algorithm, APEX, is based on machine learning and pattern recognition techniques and it 34 allows finding the coordinates of apexes and further characteristic points of hyperbolas in 35 radargrams. Both software solutions are implemented in MATLAB environment. As a first step, 36 we compare the accuracy of our algorithms when applied to synthetic data, calculated by using 37 the open-source finite-difference time-domain simulator gprMax; the considered scenarios are 38 two concrete cells hosting different metallic and dielectric targets. Then, we compare the 39 accuracy of our algorithms when applied to experimental data, recorded over district heating 40 pipes in a trench, with known geometry and depth of the pipes. For the latter scenario, we have 41 also generated a gprMax radargram, matching the geometry and scanning settings of the real one: 42 both algorithms are tested on this synthetic radargram, as well. Overall, both algorithms perform 43 well and rather uniformly in localizing the targets; the accuracy of the algorithms is at centimeter 44 level, which is sufficient in most applications.