Single spectral imagery and faster R-CNN to identify hazardous and noxious substances spills

• Published in: Environmental pollution (1987) 2020-03, Vol.258, p.113688, Article 113688
• Main Authors: Huang, Hui, Wang, Chao, Liu, Shuchang, Sun, Zehao, Zhang, Dejun, Liu, Caicai, Jiang, Yang, Zhan, Shuyue, Zhang, Haofei, Xu, Ren
• Format: Article
• Language: English
• Subjects: Faster R-CNN; Hazardous and noxious substances; Hazardous Substances - analysis; Hydrocarbons - analysis; Hyperspectral imaging; Neural Networks, Computer; Petroleum Pollution - analysis; Spectral imagery; Spectrum Analysis; Spill response; Water Pollution, Chemical - analysis
• ISSN: 0269-7491; 1873-6424
• DOI: 10.1016/j.envpol.2019.113688

The automatic identification (location, segmentation, and classification) by UAV- based optical imaging of spills of transparent floating Hazardous and Noxious Substances (HNS) benefits the on-site response to spill incidents, but it is also challenging. With a focus on the on-site optical imaging of HNS, this study explores the potential of single spectral imaging for HNS identification using the Faster R-CNN architecture. Images at 365 nm (narrow UV band), blue channel images (visible broadband of ∼400–600 nm), and RGB images of typical HNS (benzene, xylene, and palm oil) in different scenarios were studied with and without Faster R-CNN. Faster R-CNN was applied to locate and classify the HNS spills. The segmentation using Faster R-CNN-based methods and the original masking methods, including Otsu, Max entropy, and the local fuzzy thresholding method (LFTM), were investigated to explore the optimal wavelength and corresponding image processing method for the optical imaging of HNS. We also compared the classification and segmentation results of this study with our previously published studies on multispectral and whole spectral images. The results demonstrated that single spectral UV imaging at 365 nm combined with Faster R-CNN has great potential for the automatic identification of transparent HNS floating on the surface of the water. RGB images and images using Faster R-CNN in the blue channel are capable of HNS segmentation. [Display omitted] •Faster R-CNN assisted single spectral imaging is capable of HNS spill identification.•UV images at 365 nm outperformed RGB and blue channel images in HNS identification.•Faster R-CNN helps to overcome the challenges of optical imaging for HNS.•Waveband has great effect on the identification efficiency of HNS spectral imaging. Main findings: 1. Faster R-CNN-assisted single spectral imaging is capable of HNS spill identification, i.e. location, classification, and segmentation. 2. UV images at 365 nm outperformed RGB and blue-channel images in Faster R-CNN-based HNS identification.