{"title":"通过锥杆单元模块和 WIOU 损失改进的重新参数化水下物体探测网络","authors":"Xuantao Yang, Chengzhong Liu, Junying Han","doi":"10.1007/s40747-024-01533-w","DOIUrl":null,"url":null,"abstract":"<p>To overcome the challenges in underwater object detection across diverse marine environments—marked by intricate lighting, small object presence, and camouflage—we propose an innovative solution inspired by the human retina's structure. This approach integrates a cone-rod cell module to counteract complex lighting effects and introduces a reparameterized multiscale module for precise small object feature extraction. Moreover, we employ the Wise Intersection Over Union (WIOU) technique to enhance camouflage detection. Our methodology simulates the human eye's cone and rod cells' brightness and color perception using varying sizes of deep and ordinary convolutional kernels. We further augment the network's learning capability and maintain model lightness through structural reparameterization, incorporating multi-branching and multiscale modules. By substituting the Complete Intersection Over Union (CIOU) with WIOU, we increase penalties for low-quality samples, mitigating the effect of camouflaged information on detection. Our model achieved a MAP_0.75 of 72.5% on the Real-World Underwater Object Detection (RUOD) dataset, surpassing the leading YOLOv8s model by 5.8%. Additionally, the model's FLOPs and parameters amount to only 10.62 M and 4.62B, respectively, which are lower than most benchmark models. The experimental outcomes affirm our design's efficacy in addressing underwater object detection's various disturbances, offering valuable technical insights for related oceanic image processing challenges.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":5.0000,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Reparameterized underwater object detection network improved by cone-rod cell module and WIOU loss\",\"authors\":\"Xuantao Yang, Chengzhong Liu, Junying Han\",\"doi\":\"10.1007/s40747-024-01533-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>To overcome the challenges in underwater object detection across diverse marine environments—marked by intricate lighting, small object presence, and camouflage—we propose an innovative solution inspired by the human retina's structure. This approach integrates a cone-rod cell module to counteract complex lighting effects and introduces a reparameterized multiscale module for precise small object feature extraction. Moreover, we employ the Wise Intersection Over Union (WIOU) technique to enhance camouflage detection. Our methodology simulates the human eye's cone and rod cells' brightness and color perception using varying sizes of deep and ordinary convolutional kernels. We further augment the network's learning capability and maintain model lightness through structural reparameterization, incorporating multi-branching and multiscale modules. By substituting the Complete Intersection Over Union (CIOU) with WIOU, we increase penalties for low-quality samples, mitigating the effect of camouflaged information on detection. Our model achieved a MAP_0.75 of 72.5% on the Real-World Underwater Object Detection (RUOD) dataset, surpassing the leading YOLOv8s model by 5.8%. Additionally, the model's FLOPs and parameters amount to only 10.62 M and 4.62B, respectively, which are lower than most benchmark models. The experimental outcomes affirm our design's efficacy in addressing underwater object detection's various disturbances, offering valuable technical insights for related oceanic image processing challenges.</p>\",\"PeriodicalId\":10524,\"journal\":{\"name\":\"Complex & Intelligent Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.0000,\"publicationDate\":\"2024-07-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Complex & Intelligent Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s40747-024-01533-w\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Complex & Intelligent Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s40747-024-01533-w","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
摘要
为了克服在各种海洋环境中进行水下物体检测所面临的挑战--复杂的光照、小物体的存在以及伪装--我们从人类视网膜的结构中汲取灵感,提出了一种创新的解决方案。这种方法集成了一个锥杆细胞模块,以抵消复杂的光照效应,并引入了一个重新参数化的多尺度模块,用于精确提取小物体特征。此外,我们还采用了 Wise Intersection Over Union(WIOU)技术来增强伪装检测。我们的方法使用不同大小的深度卷积核和普通卷积核模拟人眼锥状细胞和杆状细胞的亮度和颜色感知。我们通过结构重参数化,结合多分支和多尺度模块,进一步增强了网络的学习能力,并保持了模型的轻盈性。通过用 WIOU 代替完全交叉联合(CIOU),我们增加了对低质量样本的惩罚,减轻了伪装信息对检测的影响。我们的模型在真实世界水下物体检测(RUOD)数据集上的 MAP_0.75 达到 72.5%,比领先的 YOLOv8s 模型高出 5.8%。此外,该模型的 FLOPs 和参数分别仅为 10.62 M 和 4.62 B,低于大多数基准模型。实验结果肯定了我们的设计在解决水下物体检测的各种干扰方面的功效,为相关的海洋图像处理挑战提供了宝贵的技术启示。
Reparameterized underwater object detection network improved by cone-rod cell module and WIOU loss
To overcome the challenges in underwater object detection across diverse marine environments—marked by intricate lighting, small object presence, and camouflage—we propose an innovative solution inspired by the human retina's structure. This approach integrates a cone-rod cell module to counteract complex lighting effects and introduces a reparameterized multiscale module for precise small object feature extraction. Moreover, we employ the Wise Intersection Over Union (WIOU) technique to enhance camouflage detection. Our methodology simulates the human eye's cone and rod cells' brightness and color perception using varying sizes of deep and ordinary convolutional kernels. We further augment the network's learning capability and maintain model lightness through structural reparameterization, incorporating multi-branching and multiscale modules. By substituting the Complete Intersection Over Union (CIOU) with WIOU, we increase penalties for low-quality samples, mitigating the effect of camouflaged information on detection. Our model achieved a MAP_0.75 of 72.5% on the Real-World Underwater Object Detection (RUOD) dataset, surpassing the leading YOLOv8s model by 5.8%. Additionally, the model's FLOPs and parameters amount to only 10.62 M and 4.62B, respectively, which are lower than most benchmark models. The experimental outcomes affirm our design's efficacy in addressing underwater object detection's various disturbances, offering valuable technical insights for related oceanic image processing challenges.
期刊介绍:
Complex & Intelligent Systems aims to provide a forum for presenting and discussing novel approaches, tools and techniques meant for attaining a cross-fertilization between the broad fields of complex systems, computational simulation, and intelligent analytics and visualization. The transdisciplinary research that the journal focuses on will expand the boundaries of our understanding by investigating the principles and processes that underlie many of the most profound problems facing society today.