Abstract
Automatic colorless floating hazardous and noxious substances (HNS) spill segmentation is an emerging research topic. Xylene is one of the priority HNSs since it poses a high risk of being involved in an HNS incident. This paper presents a novel algorithm for the target enhancement of xylene spills and their segmentation in ultraviolet (UV) images. To improve the contrast between targets and backgrounds (waves, sun reflections, and shadows), we developed a global background suppression (GBS) method to remove the irrelevant objects from the background, which is followed by an adaptive target enhancement (ATE) method to enhance the target. Based on the histogram information of the processed image, we designed an automatic algorithm to calculate the optimal number of clusters, which is usually manually determined in traditional cluster segmentation methods. In addition, necessary pre-segmentation processing and post-segmentation processing were adopted in order to improve the performance. Experimental results on our UV image datasets demonstrated that the proposed method can achieve good segmentation results for chemical spills from different backgrounds, especially for images with strong waves, uneven intensities, and low contrast.
Highlights
With the worldwide demand for chemicals increasing sharply, the amount of chemicals transported by sea has increased by 3.5 times in the past 20 years because of sea transport’s low costs for carrying large quantities over long distances [1]
The majority of these chemicals are categorized as hazardous and noxious substances (HNSs), which are defined as any substance other than oil that, if introduced into the marine environment, is likely to create human health hazards, harm living resources and other marine life, damage amenities, and/or interfere with other legitimate uses of the sea [2]
HNS spills have their own characteristics that are different from oil spills, especially considering the wide variety of products that may be involved [4]
Summary
With the worldwide demand for chemicals increasing sharply, the amount of chemicals transported by sea has increased by 3.5 times in the past 20 years because of sea transport’s low costs for carrying large quantities over long distances [1]. UAVs equipped with UV sensors have a great potential for the real-time detection of floating HNSs. Compared to choosing detection hardware, automatic target segmentation is a more challenging task in image understanding and computer vision due to the variety, low contrast, and uneven illumination of chemical spill images. Many methods of image segmentation have been proposed to detect targets on water surfaces, including thresholding [17], level sets [18], active contour models [19], the Mean-Shift [20], and neural networks [21]. Categories 2 and 3 set the threshold according to local explicit and implicit variants, thereby enhancing the recognition accuracy of pixels All these methods fail in the case of images with low contrast between the target and background, which is common in chemical spill images.
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call