All-in-one aerial image enhancement network for forest scenes

Frontiers in Plant Science

Plant Science

Zhaoqi Chen¹  Ling Zhang²  Antoni Grau³  Chuansheng Wang³  Edmundo Guerra³  Fuquan Zhang⁴ 

[1] College of Computer and Big Data, Fuzhou University, Fuzhou, China;Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, China;College of Computer and Control Engineering, Minjiang University, Fuzhou, China;Department of Automatic Control, Polytechnic University of Catalonia, Barcelona, Spain;Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, China;College of Computer and Control Engineering, Minjiang University, Fuzhou, China;Digital Media Art, Key Laboratory of Sichuan Province, Sichuan Conservatory of Music, Chengdu, China;Fuzhou Technology Innovation Center of Intelligent Manufacturing information System, Minjiang University, Fuzhou, China;Engineering Research Center for Intangible Cultural Heritage (ICH) Digitalization and Multi-source Information Fusion (Fujian Polytechnic Normal University), Fujian Province University, Fuzhou, China;

关键词: image enhancement;    all-in-one network;    multi-receptive fields;    drone image monitoring;    forest protection;    smoke detection;   

DOI  :  10.3389/fpls.2023.1154176

 received in 2023-01-30, accepted in 2023-02-28,  发布年份 2023

来源: Frontiers

【 摘 要 】

Drone monitoring plays an irreplaceable and significant role in forest firefighting due to its characteristics of wide-range observation and real-time messaging. However, aerial images are often susceptible to different degradation problems before performing high-level visual tasks including but not limited to smoke detection, fire classification, and regional localization. Recently, the majority of image enhancement methods are centered around particular types of degradation, necessitating the memory unit to accommodate different models for distinct scenarios in practical applications. Furthermore, such a paradigm requires wasted computational and storage resources to determine the type of degradation, making it difficult to meet the real-time and lightweight requirements of real-world scenarios. In this paper, we propose an All-in-one Image Enhancement Network (AIENet) that can restore various degraded images in one network. Specifically, we design a new multi-scale receptive field image enhancement block, which can better reconstruct high-resolution details of target regions of different sizes. In particular, this plug-and-play module enables it to be embedded in any learning-based model. And it has better flexibility and generalization in practical applications. This paper takes three challenging image enhancement tasks encountered in drone monitoring as examples, whereby we conduct task-specific and all-in-one image enhancement experiments on a synthetic forest dataset. The results show that the proposed AIENet outperforms the state-of-the-art image enhancement algorithms quantitatively and qualitatively. Furthermore, extra experiments on high-level vision detection also show the promising performance of our method compared with some recent baselines.

【 授权许可】

Unknown   
Copyright © 2023 Chen, Wang, Zhang, Zhang, Grau and Guerra