Object detection has so far achieved great success. However, almost all of current state-of-the-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.

This paper focuses on single image derain, which aims to restore clear image from single rain image. Through full consideration of different frequency information preservation and the complicated interactions between rain-streaks and background, a novel end-to-end cumulative rain-density sensing network (CRDNet) is proposed for adaptive rain-streaks removal. An effective W-Net with powerful learning ability is proposed as a key component to recover rain-invariant low-frequency signals. A cumulative rain-density classifier with a novel cost-sensitive label encoding strategy is proposed as an auxiliary network to improve discriminative power of extracted high-frequency rain-streaks through multi-task training. The proposed CRDNet has been compared with state-of-the-art methods on two public datasets. The quantitative and visual experimental results demonstrate that it can achieve excellent performance with great improvement. Related source code and models are available on github https://github.com/peylnog/CRDNet.

To discover relationships and associations rapidly in large-scale datasets, we propose a cross-platform tool for the rapid computation of the maximal information coefficient based on parallel computing methods. Through parallel processing, the provided tool can effectively analyze large-scale biological datasets with a markedly reduced computing time. The experimental results show that the proposed tool is notably fast, and is able to perform an all-pairs analysis of a large biological dataset using a normal computer. The source code and guidelines can be downloaded from https://github.com/HelloWorldCN/RapidMic .