Digital inspections methods have gained increasing interest in the civil engineering field for assessing the condition of various structures including buildings and bridges. These methods are used primarily to evaluate the external surface condition of the structure. However, in the case of bridges, their application when inspecting the interior of these structures remains uncommon. This paper presents a case study for inspecting the interior of a concrete box girder bridge, the Strängnäs bridge. The inspection involved gathering data using a commercial drone, creating a photogrammetrical model, detecting and quantifying damages by employing a Convolutional Neural Network (CNN). The analysis successfully detected 60 cm long concrete cracks, a total area of 3.5 m2 leakage and corrosion over 40 cm. The study addressed the difficulties such as insufficient lighting, lack of GPS signal and dust clouds reducing visibility. Despite these obstacles, the study demonstrated the effectiveness of indoor drone-based inspections.