Nighttime object detection is challenging because the low-light illumination and strong noise usually degrade the performance of feature learning. Some studies attempt to improve object detection at nighttime by enhancing the visual quality of input images, but this does not always improve the performance. In this work, we propose a simple strategy to improve the performance of nighttime object detection by directly strengthening feature representations of nighttime scenes with multiple context-related auxiliary learning tasks. Firstly, separate object localization and categorization tasks are used to refine the scene’s spatial information and object features. Meanwhile, a co-occurrence prediction task is designed to capture context relationships among objects. Finally, we employ a low-pass noise filter module to alleviate noise interference in feature learning. Experimental results, evaluated on nighttime and rainy-night scenes, demonstrate that the proposed method significantly improves the performance of nighttime object detection when used with typical object detection frameworks.