CM-YOLO: Typical Object Detection Method in Remote Sensing Cloud and Mist Scene Images

• Published in: Remote sensing (Basel, Switzerland) Vol. 17; no. 1; p. 125
• Main Authors: Hu, Jianming, Wei, Yangyu, Chen, Wenbin, Zhi, Xiyang, Zhang, Wei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2025
• Subjects: Accuracy; Aircraft; aircraft and ship detection; Artificial neural networks; background suppression; cloud and mist interferences; Clouds; Decoupling; Deep learning; Detectors; False alarms; Marine technology; Methods; Mines and mineral resources; Mining; Mist; Modules; Neural networks; Object recognition; optical image; Remote monitoring; Remote sensing; Safety management; semantic joint mining; Semantics; Target detection; Telematics; Weather
• DOI: 10.3390/rs17010125
• ISSN: 2072-4292

Remote sensing target detection technology in cloud and mist scenes is of great significance for applications such as marine safety monitoring and airport traffic management. However, the degradation and loss of features caused by the obstruction of cloud and mist elements still pose a challenging problem for this technology. To enhance object detection performance in adverse weather conditions, we propose a novel target detection method named CM-YOLO that integrates background suppression and semantic context mining, which can achieve accurate detection of targets under different cloud and mist conditions. Specifically, a component-decoupling-based background suppression (CDBS) module is proposed, which extracts cloud and mist components based on characteristic priors and effectively enhances the contrast between the target and the environmental background through a background subtraction strategy. Moreover, a local-global semantic joint mining (LGSJM) module is utilized, which combines convolutional neural networks (CNNs) and hierarchical selective attention to comprehensively mine global and local semantics, achieving target feature enhancement. Finally, the experimental results on multiple public datasets indicate that the proposed method realizes state-of-the-art performance compared to six advanced detectors, with mAP, precision, and recall indicators reaching 85.5%, 89.4%, and 77.9%, respectively.