Despite enormous observational effort by numerous space surveillance networks, the population of small (<10 cm) debris particles above LEO is still not well understood. At higher altitudes, the situation is even worse. A recent survey probing the GEO region for debris particles down to 10 cm has revealed that almost every observed target smaller than 1 m could not be associated with a known object. The size of the facilities that are needed to observe small orbital debris on high altitudes make it currently impossible to track these objects as reliably as is the case for debris in LEO. However, this does not mean that these particles are not observed: there are many large telescopes with sophisticated wide-field imaging devices that gather large amounts of data every night. And although these observatories are designed to address fundamental scientific questions in the fields of cosmology, galaxy and star formation and evolution, and planetary science, orbital debris has left its traces on the acquired data. For example, the data acquired over the last decade by the 2.65 m ESO VLT Survey Telescope at Cerro Paranal and the 4 m Blanco Telescope at Cerro Tololo consist of almost one million individual images, corresponding to 3.4 years of exposure time and resulting in over 1 Petabyte of data. To obtain a better picture of the evolution and current state of the orbital debris population, we are developing novel methods to efficiently extract observations of satellites, debris and Solar System objects from such large astronomical archives. We are currently evaluating different techniques for detecting the characteristic streaks that these objects cause on the images, such as traditional Hough transform based methods and machine-learning approaches. However, we focus not only on detecting these objects but also on performing photometric analysis that will allow us to determine the attitude, size and possibly the compositional properties of the observed objects. We will present first results that demonstrate the sensibility and efficiency of the detection algorithms as well as first light curves measured from the detected streaks. Although we focus on space debris, the methods developed are also applied to the detection of near-Earth objects, which cause similar features on the images. The results of our work are of great importance for the selection of targets for future active space debris removal missions and contribute to a better understanding of the size-frequency distribution of debris particles, which is crucial for the development of strategies to maintain the usability of Earth's orbit for future satellite missions.