{"title":"Multimodal Feature-Guided Pretraining for RGB-T Perception","authors":"Junlin Ouyang;Pengcheng Jin;Qingwang Wang","doi":"10.1109/JSTARS.2024.3454054","DOIUrl":null,"url":null,"abstract":"Wide-range multiscale object detection for multispectral scene perception from a drone perspective is challenging. Previous RGB-T perception methods directly use backbone pretrained on RGB for thermal infrared feature extraction, leading to unexpected domain shift. We propose a novel multimodal feature-guided masked reconstruction pretraining method, named M2FP, aimed at learning transferable representations for drone-based RGB-T environmental perception tasks without domain bias. This article includes two key innovations as follows. 1) We design a cross-modal feature interaction module in M2FP, which encourages modality-specific backbones to actively learn cross-modal feature representations and avoid modality bias issues. 2) We design a global-aware feature interaction and fusion module suitable for various downstream tasks, which enhances the model's environmental perception from a global perspective in wide-range drone-based scenes. We fine-tune M2FP on the drone-based object detection dataset (DroneVehicle) and semantic segmentation dataset (Kust4K). On these two tasks, compared to the second-best methods, M2FP achieves state-of-the-art performance, with an improvement of 1.8% in mean average precision and 0.9% in mean intersection over union, respectively.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10663834","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10663834/","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Wide-range multiscale object detection for multispectral scene perception from a drone perspective is challenging. Previous RGB-T perception methods directly use backbone pretrained on RGB for thermal infrared feature extraction, leading to unexpected domain shift. We propose a novel multimodal feature-guided masked reconstruction pretraining method, named M2FP, aimed at learning transferable representations for drone-based RGB-T environmental perception tasks without domain bias. This article includes two key innovations as follows. 1) We design a cross-modal feature interaction module in M2FP, which encourages modality-specific backbones to actively learn cross-modal feature representations and avoid modality bias issues. 2) We design a global-aware feature interaction and fusion module suitable for various downstream tasks, which enhances the model's environmental perception from a global perspective in wide-range drone-based scenes. We fine-tune M2FP on the drone-based object detection dataset (DroneVehicle) and semantic segmentation dataset (Kust4K). On these two tasks, compared to the second-best methods, M2FP achieves state-of-the-art performance, with an improvement of 1.8% in mean average precision and 0.9% in mean intersection over union, respectively.
期刊介绍:
The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.