重庆大学学报2024，Vol.47Issue(4)：114-126,13.DOI:10.11835/j.issn.1000-582X.2024.04.010
面向智能航道巡检的水面目标检测算法
A novel water surface target detection algorithm for intelligent waterway inspection
摘要
Abstract
To address the challenges posed by environmental noise,complex water surface target distributions,and the blurring of small-scale features in water surface target detection against complex river backgrounds,this paper presents UltraWS,an enhanced water surface target detection algorithm that integrates multi-scale features and attention mechanisms.Firstly,a spatial attention module and multi-head strategy are incorporated into a standard detection network to fuse multi-scale features and improve the detection capability of small targets.Secondly,the UltraLU module is introduced to enhance class activation mapping and reduce the influence of environmental and distribution factors on target detection.Finally,a Tucker tensor decomposition method is applied to achieve model lightweighting,enhancing model interpretability and inference speed.Experimental results demonstrate that the proposed UltraWS algorithm improves resistance to background noise,enhances small target detection,and achieves a balance between detection speed and accuracy suitable for edge deployment requirements.On the WSODD dataset,the algorithm achieves the highest mAP value of 84.5%,outperforming other mainstream methods by a considerable improvement.This proposed algorithm,coupled with the established channel safety inspection system and evaluation method,contributes significantly to the advancement of intelligent river transportation.关键词
水面目标检测/注意力机制/类激活映射/张量分解Key words
water surface target detection/attention mechanism/class activation mapping/tensor decomposition分类
信息技术与安全科学引用本文复制引用
任思羽,黄琦麟,左良栋,吴瑞,蔡枫林..面向智能航道巡检的水面目标检测算法[J].重庆大学学报,2024,47(4):114-126,13.基金项目
2021年重庆市本科院校与中国科学院科研院所合作项目(HZ2021015) （HZ2021015）
重庆市教委科学技术研究重点资助项目(KJZD-K202305201).Supported by Scooperation Project between Chongqing Municipal Undergraduate Universities and Institutes Affiliated to the Chinese Academy of Sciences in 2021(HZ2021015)and Key Project of Science and Technology Research of Chongqing Education Commission(KJZD-K202305201). （KJZD-K202305201）
