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[会议论文]

A Correction-Based Dynamic Enhancement Framework Towards Underwater Detection

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author:

Qiu, Yanling (Qiu, Yanling.) [1] | Feng, Qianxue (Feng, Qianxue.) [2] | Cai, Boqin (Cai, Boqin.) [3] | Unfold

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EI Scopus

Abstract:

To assist underwater object detection for better performance, image enhancement technology is often used as a pre-processing step. However, most of the existing enhancement methods tend to pursue the visual quality of an image, instead of providing effective help for detection tasks. In fact, image enhancement algorithms should be optimized with the goal of utility improvement. In this paper, to adapt to the underwater detection tasks, we proposed a lightweight dynamic enhancement algorithm using a contribution dictionary to guide low-level corrections. Dynamic solutions are designed to capture differences in detection preferences. In addition, it can also balance the inconsistency between the contribution of correction operations and their time complexity. Experimental results in real underwater object detection tasks show the superiority of our proposed method in both generalization and real-time performance. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Keyword:

Image enhancement Object detection Object recognition

Community:

  • [ 1 ] [Qiu, Yanling]Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information, Fuzhou University, Fuzhou; 350108, China
  • [ 2 ] [Feng, Qianxue]Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information, Fuzhou University, Fuzhou; 350108, China
  • [ 3 ] [Cai, Boqin]Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information, Fuzhou University, Fuzhou; 350108, China
  • [ 4 ] [Wei, Hongan]Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information, Fuzhou University, Fuzhou; 350108, China
  • [ 5 ] [Chen, Weiling]Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information, Fuzhou University, Fuzhou; 350108, China

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ISSN: 1865-0929

Year: 2023

Volume: 1766 CCIS

Page: 203-216

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 1

30 Days PV: 2

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管理员  2024-10-25 17:32:03  追加

管理员  2024-09-25 16:08:59  追加

管理员  2024-09-17 01:15:16  更新被引

管理员  2024-08-28 17:12:29  更新被引

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