Object detection has so far achieved great success. However, almost all of current state-ofthe-art methods focus on images with normal illumination, while object detection under low-illumination is often ignored. In this paper, we have extensively investigated several important issues related to the challenge low-illumination detection task, such as the importance of illumination on detection, the applicabilities of illumination enhancement on low-illumination object detection task, and the influences of illumination balanced dataset and model's parameters initialization, etc. We further have proposed a Night Vision Detector (NVD) with specifically designed feature pyramid network and context fusion network for object detection under low-illuminance. Through conducting comprehensive experiments on a public real low-illuminance scene dataset ExDARK and a selected normal-illumination counterpart COCO*, we on one hand have reached some valuable conclusions for reference, on the other hand, have found specific solutions for low-illumination object detection. Our strategy improves detection performance by 0.5%~2.8% higher than basic model on all standard COCO evaluation criterions. Our work can be taken as effective baseline and shed light to future studies on low-illumination detection.

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