Boulder-sized clasts are common on the surface of Mars, and many are sufficiently large to be resolved by the High Resolution Imaging Science Experiment (HiRISE) camera aboard the Mars Reconnaissance Orbiter (MRO). The size, number, and location of boulders on the surface and their spatial distribution can reveal the processes that have operated on the surface, including boulder erosion, burial, impact excavation, and other mechanisms of boulder transport and generation. However, quantitative analysis of statistically significant boulder populations which could inform these processes entails prohibitively laborious manual segmentation, granulometry and morphometry measurements over large areas. Here we develop and describe an automated tool to locate and measure boulders on the martian surface: the Martian Boulder Automatic Recognition System (MBARS). The open-source Python-based toolkit autonomously measures boulder diameter and height in HiRISE images enabling rapid and accurate assessments of boulder populations. We compare our algorithm with existing boulder-counting methodologies, manual analyses, and objects of known size to verify accuracy and precision. Additionally, we test MBARS quantitatively characterizing boulders around an impact crater in the martian northern lowlands. We compare this to previous work on rock excavation during impact cratering using manually counted boulders around lunar craters.