PE-YOLO: Pyramid Enhancement Network for Dark Object Detection

Current object detection models have achieved good results on many benchmark datasets, detecting objects in dark conditions remains a large challenge. To address this issue, we propose a pyramid enhanced network (PENet) and joint it with YOLOv3 to build a dark object detection framework named PE-YOLO. Firstly, PENet decomposes the image into four components of different resolutions using the Laplacian pyramid. Specifically we propose a detail processing module (DPM) to enhance the detail of images, which consists of context branch and edge branch. In addition, we propose a low-frequency enhancement filter (LEF) to capture low-frequency semantics and prevent high-frequency noise. PE-YOLO adopts an end-to-end joint training approach and only uses normal detection loss to simplify the training process. We conduct experiments on the low-light object detection dataset ExDark to demonstrate the effectiveness of ours. The results indicate that compared with other dark detectors and low-light enhancement models, PE-YOLO achieves the advanced results, achieving 78.0% in mAP and 53.6 in FPS, respectively, which can adapt to object detection under different low-light conditions. The code is available at https://github.com/XiangchenYin/PE-YOLO.Comment: Accepted at ICANN 202

DEFormer: DCT-driven Enhancement Transformer for Low-light Image and Dark Vision

The goal of low-light image enhancement is to restore the color and details of the image and is of great significance for high-level visual tasks in autonomous driving. However, it is difficult to restore the lost details in the dark area by relying only on the RGB domain. In this paper we introduce frequency as a new clue into the network and propose a novel DCT-driven enhancement transformer (DEFormer). First, we propose a learnable frequency branch (LFB) for frequency enhancement contains DCT processing and curvature-based frequency enhancement (CFE). CFE calculates the curvature of each channel to represent the detail richness of different frequency bands, then we divides the frequency features, which focuses on frequency bands with richer textures. In addition, we propose a cross domain fusion (CDF) for reducing the differences between the RGB domain and the frequency domain. We also adopt DEFormer as a preprocessing in dark detection, DEFormer effectively improves the performance of the detector, bringing 2.1% and 3.4% improvement in ExDark and DARK FACE datasets on mAP respectively.Comment: submit to ICRA202

Optical Properties of Aerosols and Chemical Composition Apportionment under Different Pollution Levels in Wuhan during January 2018

To clarify the aerosol optical properties under different pollution levels and their impacting factors, hourly organic carbon (OC), elemental carbon (EC), and water-soluble ion (WSI) concentrations in PM2.5 were observed by using monitoring for aerosols and gases (MARGA) and a semicontinuous OC/EC analyzer (Model RT-4) in Wuhan from 9 to 26 January 2018. The aerosol extinction coefficient (bext) was reconstructed using the original Interagency Monitoring of Protected Visual Environment (IMPROVE) formula with a modification to include sea salt aerosols. A good correlation was obtained between the reconstructed bext and measured bext converted from visibility. bext presented a unimodal distribution on polluted days (PM2.5 mass concentrations > 75 μg⋅m−3), peaking at 19:00. bext on clean days (PM2.5 mass concentrations < 75 μg⋅m−3) did not change much during the day, while on polluted days, it increased rapidly starting at 12:00 due to the decrease of wind speed and increase of relative humidity (RH). PM2.5 mass concentrations, the aerosol scattering coefficient (bscat), and the aerosol extinction coefficient increased with pollution levels. The value of bext was 854.72 Mm−1 on bad days, which was 4.86, 3.1, 2.29, and 1.28 times of that obtained on excellent, good, acceptable, and poor days, respectively. When RH < 95%, bext exhibited an increasing trend with RH under all pollution levels, and the higher the pollution level, the bigger the growth rate was. However, when RH > 95%, bext on acceptable, poor and bad days decreased, while bext on excellent and good days still increased. The overall bext in Wuhan in January was mainly contributed by NH4NO3 (25.2%) and organic matter (20.1%). The contributions of NH4NO3 and (NH4)2SO4 to bext increased significantly with pollution levels. On bad days, NH4NO3 and (NH4)2SO4 contributed the most to bext, accounting for 38.2% and 27.0%, respectively

Characteristics of Aerosol during a Severe Haze-Fog Episode in the Yangtze River Delta: Particle Size Distribution, Chemical Composition, and Optical Properties

Particle size distribution, water soluble ions, and black carbon (BC) concentration in a long-term haze-fog episode were measured using a wide-range particle spectrometer (WPS), a monitor for aerosols and gases (MARGA), and an aethalometer (AE33) in Nanjing from 16 to 27 November, 2018. The observation included five processes of clean, mist, mix, haze, and fog. Combined with meteorological elements, the HYSPLIT model, and the IMPROVE model, we analyzed the particle size distribution, chemical composition, and optical properties of aerosols in different processes. The particle number size distribution (PNSD) in five processes differed: It was bimodal in mist and fog and unimodal in clean, mix, and haze. The particle surface area size distribution (PSSD) in different processes showed a bimodal distribution, and the second peak of the mix and fog processes shifted to a larger particle size at 480 nm. The dominant air masses in five processes differed and primarily originated in the northeast direction in the clean process and the southeast direction in the haze process. In the mist, mix, and fog processes local air masses dominated. NO3− was the primary component of water soluble ions, with the lowest proportion of 45.6% in the clean process and the highest proportion of 53.0% in the mix process. The ratio of NH4+ in the different processes was stable at approximately 23%. The ratio of SO42− in the clean process was 26.2%, and the ratio of other processes was approximately 20%. The average concentration of BC in the fog processes was 10,119 ng·m−3, which was 3.55, 1.80, 1.60, and 1.46 times that in the processes of clean, mist, mix, and haze, respectively. In the different processes, BC was primarily based on liquid fuel combustion. NO3−, SO42−, and BC were the main contributors to the atmospheric extinction coefficient and contributed more than 90% in different processes. NO3− contributed 398.43 Mm−1 in the mix process, and SO42− and BC contributed 167.90 Mm−1 and 101.19 Mm−1, respectively, during the fog process