{"title":"低光照条件和动态场景下建筑工地的自监督单目深度估计","authors":"","doi":"10.1016/j.autcon.2024.105848","DOIUrl":null,"url":null,"abstract":"<div><div>Estimating construction scene depth from a single image is crucial for various downstream tasks. Self-supervised monocular depth estimation methods have recently achieved impressive results and demonstrated state-of-the-art performance. However, the low-light conditions and dynamic scenes on construction sites pose significant challenges to these methods, hindering their practical deployment. Therefore, an architecture called LLD-Depth is presented to address these challenges, including an improved ForkGAN model to generate paired low-light images from clear-day images, a new unifying learning method for accurately estimating monocular depth, motion flow, camera ego-motion, and its intrinsic parameters, as well as a training framework to estimate monocular depth under both low-light and clear-day conditions effectively. Finally, the effectiveness of monocular depth estimation in construction scenes is verified. LLD-Depth brings 16.67% and 20.17% gain in relative mean error for clear-day and low-light scenes and 2.60% and 1.80% gain in average order accuracy, achieving state-of-the-art performance.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":null,"pages":null},"PeriodicalIF":9.6000,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Self-supervised monocular depth estimation on construction sites in low-light conditions and dynamic scenes\",\"authors\":\"\",\"doi\":\"10.1016/j.autcon.2024.105848\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Estimating construction scene depth from a single image is crucial for various downstream tasks. Self-supervised monocular depth estimation methods have recently achieved impressive results and demonstrated state-of-the-art performance. However, the low-light conditions and dynamic scenes on construction sites pose significant challenges to these methods, hindering their practical deployment. Therefore, an architecture called LLD-Depth is presented to address these challenges, including an improved ForkGAN model to generate paired low-light images from clear-day images, a new unifying learning method for accurately estimating monocular depth, motion flow, camera ego-motion, and its intrinsic parameters, as well as a training framework to estimate monocular depth under both low-light and clear-day conditions effectively. Finally, the effectiveness of monocular depth estimation in construction scenes is verified. LLD-Depth brings 16.67% and 20.17% gain in relative mean error for clear-day and low-light scenes and 2.60% and 1.80% gain in average order accuracy, achieving state-of-the-art performance.</div></div>\",\"PeriodicalId\":8660,\"journal\":{\"name\":\"Automation in Construction\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":9.6000,\"publicationDate\":\"2024-11-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Automation in Construction\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0926580524005843\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Automation in Construction","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0926580524005843","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
Self-supervised monocular depth estimation on construction sites in low-light conditions and dynamic scenes
Estimating construction scene depth from a single image is crucial for various downstream tasks. Self-supervised monocular depth estimation methods have recently achieved impressive results and demonstrated state-of-the-art performance. However, the low-light conditions and dynamic scenes on construction sites pose significant challenges to these methods, hindering their practical deployment. Therefore, an architecture called LLD-Depth is presented to address these challenges, including an improved ForkGAN model to generate paired low-light images from clear-day images, a new unifying learning method for accurately estimating monocular depth, motion flow, camera ego-motion, and its intrinsic parameters, as well as a training framework to estimate monocular depth under both low-light and clear-day conditions effectively. Finally, the effectiveness of monocular depth estimation in construction scenes is verified. LLD-Depth brings 16.67% and 20.17% gain in relative mean error for clear-day and low-light scenes and 2.60% and 1.80% gain in average order accuracy, achieving state-of-the-art performance.
期刊介绍:
Automation in Construction is an international journal that focuses on publishing original research papers related to the use of Information Technologies in various aspects of the construction industry. The journal covers topics such as design, engineering, construction technologies, and the maintenance and management of constructed facilities.
The scope of Automation in Construction is extensive and covers all stages of the construction life cycle. This includes initial planning and design, construction of the facility, operation and maintenance, as well as the eventual dismantling and recycling of buildings and engineering structures.