Dedark+Detection: A Hybrid Scheme for Object Detection under Low-light Surveillance

Abstract

Object detection under low-light surveillance is a crucial problem that less efforts have been made on it. In this paper, we proposed a hybrid method that jointly use enhancement and object detection for the above challenge, namely Dedark+Detection. In this method, the low-light surveillance video is processed by the proposed de-dark method, and the video can thus be converted to appearance under normal lighting condition. This enhancement bring more benefits to the subsequent stage of object detection. After that, an object detection network is trained on the enhanced dataset for practical applications under low-light surveillance. Experiments are performed on 18 low-light surveillance video test sequences, and superior performance can be found when comparing to state-of-the-arts.