Convolutional Neural Networks for object detection are increasingly executed onboard satellites to enable real-time, at-the-edge inference for Earth observation applications. In this context, the memory devices used to store these models are exposed to space radiation, which can induce bit flips in the stored weights, degrading system dependability and, in particular, the reliability of the neural networks. A promising approach to mitigate such effects is to perform a reliability assessment of neural networks prior to deployment, allowing for more informed decisions regarding their use in space missions. This thesis presents RADRELAX, a tool designed to assess the reliability of Convolutional Neural Networks performing object detection tasks.