{"title":"RTL-YOLOv8n:用于高效、准确水下目标探测的轻量级模型","authors":"Guanbo Feng, Zhixin Xiong, Hongshuai Pang, Yunlei Gao, Zhiqiang Zhang, Jiapeng Yang, Zhihong Ma","doi":"10.3390/fishes9080294","DOIUrl":null,"url":null,"abstract":"Underwater object detection is essential for the advancement of automated aquaculture operations. Addressing the challenges of low detection accuracy and insufficient generalization capabilities for underwater targets, this paper focuses on the development of a novel detection method tailored to such environments. We introduce the RTL-YOLOv8n model, specifically designed to enhance the precision and efficiency of detecting objects underwater. This model incorporates advanced feature-extraction mechanisms—RetBlock and triplet attention—that significantly improve its ability to discern fine details amidst complex underwater scenes. Additionally, the model employs a lightweight coupled detection head (LCD-Head), which reduces its computational requirements by 31.6% compared to the conventional YOLOv8n, without sacrificing performance. Enhanced by the Focaler–MPDIoU loss function, RTL-YOLOv8n demonstrates superior capability in detecting challenging targets, showing a 1.5% increase in mAP@0.5 and a 5.2% improvement in precision over previous models. These results not only confirm the effectiveness of RTL-YOLOv8n in complex underwater environments but also highlight its potential applicability in other settings requiring efficient and precise object detection. This research provides valuable insights into the development of aquatic life detection and contributes to the field of smart aquatic monitoring systems.","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":"50 1","pages":""},"PeriodicalIF":4.6000,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"RTL-YOLOv8n: A Lightweight Model for Efficient and Accurate Underwater Target Detection\",\"authors\":\"Guanbo Feng, Zhixin Xiong, Hongshuai Pang, Yunlei Gao, Zhiqiang Zhang, Jiapeng Yang, Zhihong Ma\",\"doi\":\"10.3390/fishes9080294\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Underwater object detection is essential for the advancement of automated aquaculture operations. Addressing the challenges of low detection accuracy and insufficient generalization capabilities for underwater targets, this paper focuses on the development of a novel detection method tailored to such environments. We introduce the RTL-YOLOv8n model, specifically designed to enhance the precision and efficiency of detecting objects underwater. This model incorporates advanced feature-extraction mechanisms—RetBlock and triplet attention—that significantly improve its ability to discern fine details amidst complex underwater scenes. Additionally, the model employs a lightweight coupled detection head (LCD-Head), which reduces its computational requirements by 31.6% compared to the conventional YOLOv8n, without sacrificing performance. Enhanced by the Focaler–MPDIoU loss function, RTL-YOLOv8n demonstrates superior capability in detecting challenging targets, showing a 1.5% increase in mAP@0.5 and a 5.2% improvement in precision over previous models. These results not only confirm the effectiveness of RTL-YOLOv8n in complex underwater environments but also highlight its potential applicability in other settings requiring efficient and precise object detection. This research provides valuable insights into the development of aquatic life detection and contributes to the field of smart aquatic monitoring systems.\",\"PeriodicalId\":2,\"journal\":{\"name\":\"ACS Applied Bio Materials\",\"volume\":\"50 1\",\"pages\":\"\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-07-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Bio Materials\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.3390/fishes9080294\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, BIOMATERIALS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.3390/fishes9080294","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
RTL-YOLOv8n: A Lightweight Model for Efficient and Accurate Underwater Target Detection
Underwater object detection is essential for the advancement of automated aquaculture operations. Addressing the challenges of low detection accuracy and insufficient generalization capabilities for underwater targets, this paper focuses on the development of a novel detection method tailored to such environments. We introduce the RTL-YOLOv8n model, specifically designed to enhance the precision and efficiency of detecting objects underwater. This model incorporates advanced feature-extraction mechanisms—RetBlock and triplet attention—that significantly improve its ability to discern fine details amidst complex underwater scenes. Additionally, the model employs a lightweight coupled detection head (LCD-Head), which reduces its computational requirements by 31.6% compared to the conventional YOLOv8n, without sacrificing performance. Enhanced by the Focaler–MPDIoU loss function, RTL-YOLOv8n demonstrates superior capability in detecting challenging targets, showing a 1.5% increase in mAP@0.5 and a 5.2% improvement in precision over previous models. These results not only confirm the effectiveness of RTL-YOLOv8n in complex underwater environments but also highlight its potential applicability in other settings requiring efficient and precise object detection. This research provides valuable insights into the development of aquatic life detection and contributes to the field of smart aquatic monitoring systems.
期刊介绍:
ACS Applied Bio Materials is an interdisciplinary journal publishing original research covering all aspects of biomaterials and biointerfaces including and beyond the traditional biosensing, biomedical and therapeutic applications.
The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important bio applications. The journal is specifically interested in work that addresses the relationship between structure and function and assesses the stability and degradation of materials under relevant environmental and biological conditions.