Context. Transit detection algorithms are mathematical tools used for detecting planets in the photometric data of transit surveys. In this work we study their application to space-based surveys. Aims: Space missions are exploring the parameter space of the transit surveys where classical algorithms do not perform optimally, either because of the challenging signal-to-noise ratio of the signal or its non-periodic characteristics. We have developed an algorithm addressing these challenges for the mission CoRoT. Here we extend the application to the data from the space mission Kepler. We aim at understanding the performances of algorithms in different data sets. Methods: We built a simple analytical model of the transit signal and developed a strategy for the search that improves the detection performance for transiting planets. We analyzed Kepler data with a set of stellar activity filtering and transit detection tools from the CoRoT community that are designed for the search of transiting planets. Results: We present a new algorithm and its performances compared to one of the most widely used techniques in the literature using CoRoT data. Additionally, we analyzed Kepler data corresponding to quarter Q1 and compare our results with the most recent list of planetary candidates from the Kepler survey. We found candidates that went unnoticed by the Kepler team when analyzing longer data sets. We study the impact of instrumental features on the production of false alarms and false positives. These results show that the analysis of space mission data advocates the use of complementary detrending and transit detection tools also for future space-based transit surveys such as PLATO.
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